Executive Education

Data Analytics Essentials

Master in-demand tools like, Python, SQL, Power BI & More

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logo With 6-week Microsoft Power BI Data Analyst Certification Training Program delivered by Great Learning

Why choose data analytics

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11.5 million new data science and analytics jobs will be created by the year 2026
Source: Analytics Insight

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Average pay in the US is $110,000 Source: indeed
 
 

The UT Austin Advantage

Taught by World-Renowned Experts
  • Designed by the world-renowned faculty at the University of Texas at Austin
  • Customized curriculum designed to build industry-valued skills
  • Live sessions by industry experts
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In-Demand Languages, Tools, and Skills
  • Python Foundations (NumPy, Pandas, Seaborn)
  • Business statistics
  • Data Visualization (Power BI, Tableau)
  • Querying data with SQL
  • Exploratory Data Analysis
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Job-Ready in 16 Weeks
  • Apply your skills to three hands-on projects with specific industry-relevant applications
  • Enhance your resume and secure career opportunities with GreatLearning’s career support services
  • Work on projects alongside established data scientists and fellow learners worldwide
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Learn from world-renowned faculty from University of Texas at Austin
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Acquire in-demand skills in programming and tools for data analytics
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Data Analytics Essentials

Learn to leverage data and upskill in 16 Weeks

Certificate from The University of Texas at Austin

#3

MS - Business Analytics

QS World University rankings, 2022

#6

Executive Education - Custom Programs

Financial Times, 2022

zoom icon Data Analytics Essentials certificate by university of Texas at Austin

All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the University.

#3

MS - Business Analytics

QS World University rankings, 2022

#6

Executive Education - Custom Programs

Financial
Times, 2022

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KUMAR MUTHURAMAN

Faculty Director, PGP-DSBA

H. Timothy (Tim) Harkins Centennial Professor Faculty Director, Center for Research and Analytics

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MS & PhD: Stanford University

For any feedback & queries regarding the program, please reach out to us at MSB-DSBA@mccombs.utexas.edu

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Designed to empower learners with everything they need to succeed in their careers, GL Excelerate is a career support program exclusively for our program learners.

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Career Sessions

Interact personally with industry professionals to get valuable insights and guidance.

Curated Jobs
Curated Jobs

Resume & LinkedIn Profile Review

Present yourself in the best light through assets that truly showcase your strengths.

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Interview Preparation

Get an insiders’ perspective to understand what recruiters look for.

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Build an industry-ready portfolio to showcase your mastery of skills and tools.

Ace Power BI Certification

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Prepare for PL-300 Certification Exam with:

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    Live Virtual Classes with Microsoft Certified Instructors

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    Hands-on Learning and Academic Support

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    Exam Preparation Guide

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    Hands-on Projects

*Delivered by Great Learning in collaboration with Microsoft

Curriculum

Developed by a leading university, this core curriculum of the data analytics essentials course covers foundational concepts and major skills and tools required to excel as a data analyst.

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Pre-work

Here, we will quickly learn all the prerequisites required to learn the fundamentals of data analytics, such as Excel, Python Programming, and Descriptive Statistics.

Introduction to Excel

    The first module of this data analytics course for beginners will cover the basics of Microsoft Excel. Students will learn data analysis essentials using Excel to create and format spreadsheets, along with CSV, tables, formulae, sorting, filtering, and much more.

  • Why Excel? What are the advantages of Excel?

    • Here, students will learn why Excel is a powerful spreadsheet application for analyzing and manipulating data and the advantages of using Excel for business and personal use.

  • CSV File Format

    • CSV files can be used with almost any spreadsheet program, such as Microsoft Excel, Apache Openoffice Calc, or Google Sheets. Here, students will learn how to use CSV for exchanging data between different applications.

  • Tools, Ribbons, Commands

    • In this, students will learn how to add functionality to a workbook and make working with data easier using tools, ribbons, and commands available in Excel.

  • Cell Referencing

    • This topic will teach students the process of cell referencing, a powerful feature in Excel that allows them to link data from multiple sheets and workbooks.

  • Tables

    • This topic will teach students how to implement tables in Excel to organize data and make it easy to view and understand.

  • Basic Arithmetic Functions (+,-,*,/)

    • This topic will make students familiar with implementing essential arithmetic functions to create more complex formulas that will unlock the power of Excel for data analysis needs.

  • Date Functions

    • This topic will familiarize students with implementing date functions using different formats in Excel.

  • Sorting

    • Here, students will learn how to sort data, where they can organize data in a way that makes it easier to find the information they need and to see relationships between different pieces of data

  • Filtering

    • Here, students will learn how to filter data, a powerful way in data analysis where they can easily view subsets of their data by hiding the rows that don't meet their criteria.

  • IF ELSE

    • The IF-ELSE function in Excel is a handy tool that allows us to perform different actions depending on whether a condition is met or not. This can be particularly useful when we have a large dataset and want to perform different analyses depending on specific criteria.

Descriptive Statistics

    This module will cover the basic concepts of descriptive statistics, including measures of central tendency (mean, median, and mode) and measures of dispersion (range, variance, and standard deviation).

  • Seeing patterns in the data

    • Here, students will learn how to identify and analyze patterns in the data with the assistance of descriptive statistics.

  • Sample and Population

    • Here, students will gain an understanding of several concepts in probability, such as sample and population.

  • Central Tendency (Mean, Median, Mode)

    • This topic will make students familiar with measures of central tendency (mean, median, and mode) to help them calculate the average, find the median value of a dataset, and find the most frequent value.

  • Dispersion (Range, Variance, Standard Deviation)

    • This topic will make students familiar with measures of dispersion (range, variance, and standard deviation), which is essential for analyzing data sets because it can give us insights into the spread of the data.

  • Five point Summary

    • In this topic, students will understand the five-point summary in descriptive statistics.

Data Analytics Foundations

Moving on to the next module of this data analytics essentials course, students will understand several fundamentals of data analysis, such as lifecycle, data pipeline, and insights generation using Excel, and apply these techniques to real-world data sets.

Analytics Life Cycle - An end to end use case

    Industry 4.0 is the term used to describe the fourth industrial revolution, and data is the lifeblood of Industry 4.0. In this module, students will explore the world of data and how data is critical for the industrial revolution.

  • Introduction to Analytics Lifecycle

    • In this chapter, students will go through the various phases involved in the data analytics lifecycle.

  • Datasources and Databases

    • Datasources are the information repositories that hold the data sets that analysts utilize to perform their work.

  • A typical Data Pipeline

    • This chapter will familiarize students with the data pipeline, a series of steps to ingest, transform and analyze raw data.

  • Insight generation and Recommendation

    • Here, students will familiarize themselves with the process of analyzing data to discover trends and patterns that can be used to generate new insights and make recommendations.

  • End-to-end Business Case Study Demo

    • Here, students will go through a hands-on demo of an end-to-end business case study.

Generating Insights using Excel

    In this module, students will explore the process of generating insights in multiple ways using Excel, such as tables, tabs, charts, and descriptive statistics.

  • Pivot Tables

    • This topic will make students understand pivot tables, which allow them to quickly summarize large amounts of data in a concise, easy-to-understand format.

  • Sorting Data in Pivot Tables

    • Here, students will learn how to sort data in pivot tables, where they can sort by values, by column, by row, and by multiple columns and rows.

  • Filtering Data in Pivot Tables

    • Here, students will learn how to filter data in pivot tables, where they can filter by date, product, customer, or any other entity.

  • Analyse Tab

    • Here, students will learn how to work with the analyze tab in Excel, which allows them to perform various statistical analyses on their data, like calculating means, standard deviations, percentiles, etc. They can also use the tab to create charts and graphs to visualize their data.

  • Exploring charts

    • In this topic, students will explore a variety of charts available in Excel to visualize data sets in multiple formats.

  • Descriptive Statistics

    • This chapter will help students analyze and understand diverse data sets in Excel with the aid of descriptive statistics.

Data Analytics with SQL

Heading into the next chapter, students will learn everything they need to know about how to use SQL to perform data analysis effectively. By the end, they’ll be able to confidently query databases and make sense of data like a pro!

Querying data with SQL

    Querying data with SQL allows us to find and manipulate data in our database quickly. In this module, students will learn how to write and understand SQL queries to retrieve data from any database.

  • Importing a Database

    • Here, students will learn the process of importing a database into MySQL.

  • Introduction to RDBMS

    • This topic will introduce students to RDBMS, a relational database management system to create, store, update, and delete data in a relational database.

  • Selecting data

    • When working with data stored in a MySQL database, it is often necessary to select specific data in order to work with it. Here, students will learn how to select data in a variety of ways using the SELECT statement.

  • Filtering data

    • When working with databases, it is often necessary to filter data to return only the rows that meet specific criteria. Here, students will learn how to filter data and make their queries more specific using the WHERE clause.

Advanced Querying to extract business insights

    Advanced querying encompasses a variety of techniques that allow a user to manipulate data in order to answer complex business questions. In this module, students will learn the process of advanced querying to extract business insights.

  • Aggregating data

    • Students will get familiar with data aggregation in SQL, a process of combining data from multiple tables into a single table, where a calculation is performed on a set of values and returns a single value.

  • Joining data

    • Students will familiarize themselves with combining data from two or more tables into a single table using the JOIN command.

  • Window Functions

    • Here, students will learn how to identify values in a collection of rows and provide a single result for each row, which is called the window function.

  • Order-of-Execution

    • Students will be introduced to the order-of-execution technique, which defines the specific order in which the clauses, expressions, and operators in a statement are evaluated.

  • Extracting data to Excel to perform data analysis
Project Week
    Once students are done with the fundamentals of data analytics, this data analysis course for beginners will provide students with the first hands-on project on the topics learned so far.
Data-driven Insights using Python

This chapter teaches students how to use Python to gain insights from data. The course will cover how to use Python to read data from a variety of sources, how to process that data to extract useful information, and how to visualize the data to enable decision-making.

Introduction to Python Programming

    This module will give students a comprehensive introduction to the Python programming language, covering topics like Google Colab, variables, data types, data structures, conditional statements, loops, and functions.

  • Setting up Google Colab

    • Google Colab is a free notebook environment for writing and executing code. Students will learn how to set up and work with Google Colab in this section.

  • Variables

    • Here, students will learn how to work with variables in Python to store values and retrieve them later.

  • Data Types

    • Here, students will understand data types, which define the type of data that a variable can hold. There are several built-in data types in Python, including integers, floats, and strings, among others.

  • Data Structures

    • Python's standard library provides a wide range of data structures that can be used to store and efficiently organize data. The most commonly used data structures are lists, tuples, dictionaries, and sets.

  • Conditional Statements

    • This topic will familiarize students with conditional statements that help them execute the code only if the specified condition is met.

  • Loops

    • The concept of loops will be taught to the students in this chapter. Loops can execute a block of code continually until a specific condition is met, such as computing the sum of two integers or displaying multiplication or other tables, among other things.

  • Functions

    • This chapter will help students understand and use Functions using Python programming so that they may reuse code.

Data Transformation using Numpy and Pandas

    Numpy is a powerful library for performing numerical operations on arrays and matrices. At the same time, Pandas is a library for working with data frames, which are similar to tables in a relational database. In this module, we'll explore how to use these two libraries to perform various data transformation tasks.

  • Numpy Arrays

    • A Numpy array is a multidimensional array of objects of the same type, and this topic will teach students how to perform numerical operations efficiently using Numpy arrays.

  • Numpy Functions

    • This article will make students familiar with various Numpy functions that can assist them in speeding up their code.

  • Indexing

    • Students will learn how to find and retrieve data from a given data structure using Indexing in this topic.

  • Accessing

    • Here, students will learn how to access data from a Python project using the dot (.) operator.

  • Pandas Series

    • In this topic, students will understand how to hold several data types, such as numbers, strings, etc., using a one-dimensional array-like object, i.e., the Pandas Series.

  • Pandas Dataframes

    • Here, students will gain an understanding of Pandas Dataframes, which are two-dimensional, size-mutable, potentially heterogeneous tabular data structures with labeled axes (rows and columns).

  • Saving Loading

    • Here, students will explore the process of saving and loading files in multiple formats using the Pandas library.

  • Merging dataframes

    • This topic will familiarize students with the process of combining/merging two or more dataframes into a single dataframe with the help of specific methods.

  • Pandas Functions

    • This topic will familiarize students with various Pandas functions that are widely implemented in numerous applications of data science and machine learning.

Exploratory Data Analysis

    Exploratory Data Analysis, also known as EDA, uses visual techniques to help us find patterns and insights frequently inside specific data. This module will explain EDA using Python in-depth.

  • Data Sanity Checks

    • This topic will make students understand the significance of performing sanity checks to ensure that the data is clean and ready for analysis while working with data.

  • Univariate Analysis

    • The students in this topic will gain an understanding of how to perform statistical comparisons using univariate analysis.

  • Bivariate Analysis

    • The students in this topic will gain an understanding of how to perform statistical comparisons using bivariate analysis.

  • Missing Value Treatment

    • This topic will familiarize students with the number of ways to deal with missing values when performing exploratory data analysis.

  • Outlier Detection

    • This topic will familiarize students with the number of ways to detect outliers that can help identify problems and patterns in data for further analysis.

Additional Content: Data Visualization with Seaborn

    Seaborn is a powerful data visualization library that makes creating beautiful, informative visualizations easy. This module will teach students how to use Seaborn to create sophisticated visualizations, including histograms, line plots, joint charts, heatmaps, and more.

  • Histogram

    • In this topic, students will learn how to represent the distribution of numerical data in a graphical format using histograms accurately.

  • Box Plot

    • A box plot is a graph made up of a box and a whisker that shows the distribution of a data set. Here, students will get familiar with the process of showing the spread of the data and finding outliers.

  • Line Plot

    • Line plots are an excellent way to visualize relationships between numeric variables. Seaborn makes it easy to create high-quality line plots with just a few lines of code.

  • Scatter Plot

    • A scatter plot is used to indicate the data as a collection of points. Here, students will understand how Seaborn makes it easy to create a scatter plot for exploring and visualizing data.

  • Joint Plot

    • A joint plot is an excellent way to visualize the relationship between two variables. Here, students will learn about Seaborn's jointplot() function, which makes it easy to create these plots.

  • Violin Plot

    • Violin plots play the same roles as box plots and whisker plots. They show the distribution of a quantitative variable for several levels of a categorical variable and are beneficial for comparing distributions between different groups.

  • Strip Plot

    • A strip plot is a graphical representation of categorical data where a separate strip on the plot represents each category. This topic will make students understand strip plots' implementation using Seaborn to visualize the distribution of categorical data.

  • Heatmap

    • A heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. This topic will make students understand the usage of seaborn.heatmap() function, which takes in a rectangular dataset and an optional argument for specifying the color palette.

  • Plotly

    • Plotly is a powerful Python library that allows you to create interactive, publication-quality figures. This topic will teach students how to work with Plotly, which helps create line plots, bar plots, scatter plots, and more.

  • Customizing Plots

    • This comprehensive guide will show students how to tweak every aspect of their Seaborn plots to create the perfect visualization for their data. By the end of this guide, students will be experts at creating beautiful, informative Seaborn plots that tell their data's story perfectly.

Project Week
    Once students are done with the data analysis essentials, this data analyst course for beginners will provide students with the second hands-on project on the topics learned so far.
Creative Storytelling with Tableau (Self-Paced Module)

In this chapter, students will learn how to use Tableau to create impactful, interactive data visualizations that tell a story. Students will also learn how to use Tableau's visualization capabilities to tell compelling stories that engage their audience.

Storyboarding 101 with Tableau

    This comprehensive guide will cover everything students need to know about storyboarding with Tableau, from the basics to advanced tips and tricks.

  • Tableau Public Installation

    • Tableau Public is a free data visualization software that can be installed on any computer to create interactive visualizations of data. Here, students will explore the process of installing Tableau Public on their systems.

  • Dimensions & Measures

    • In Tableau, dimensions are the qualitative data elements in your data set, while measures are the quantitative data elements. In this topic, we will discuss how to use both dimensions and measures in Tableau to perform various data analysis tasks.

  • Data Types

    • This topic will cover the implementation of several data types available in Tableau, such as string, date, time, numerical, boolean, geographic, and clusters.

  • Choosing-charts w/ SHOW ME

    • This topic will teach students to use the "SHOW ME" tool, which provides a quick way to create various charts based on their selected data.

  • Calculations

    • Calculations in Tableau are an essential part of data analysis. Creating calculated fields allows us to analyze data in ways that would not be possible with the raw data alone. This topic will show us how to create and use calculated fields in Tableau.

  • Dates and Date Functions

    • Here, students will learn about dates and date functions, which allow users to create visual representations of data over time, track changes in data over time, and identify trends.

  • Filtering

    • Filtering data in Tableau is a way of isolating data points within a larger dataset that meet the specific criteria you define. There are a few different ways to filter data in Tableau, which we will discuss in this topic.

  • Dashboarding-101

    • This topic covers everything from the basics of setting up your Tableau dashboard to more advanced topics like creating custom visualizations and using filters to manipulate your data.

Tableau for building Interactive Dashboards

    Students will learn how to create stunning visualizations that tell a story and engage their audience. We'll also show students how to use Tableau's powerful features to bring their data to life.

  • Parameters

    • Here, students will learn how to filter data using parameters in Tableau.

  • Actions

    • Here, students will learn how to work with an action, an interactive element that can be used to filter and highlight data on a dashboard.

  • Sorting

    • This topic will familiarize students with a way to organize their data in a specific order with the aid of sorting.

  • Special Charts

    • In this topic, we'll explore some of the special charts that Tableau can create to communicate information effectively.

  • What-if-analysis

    • What-if-analysis in Tableau is the process of exploring data to find answers to questions you didn't know you had. Students will learn about this powerful process to gain insights into their data and make better decisions.

  • Reshaping Data

    • In this topic, we'll show students how to reshape data in Tableau and how to use the various features and tools available to make the process as easy and efficient as possible.

  • Level-of-detail

    • In this topic, we will learn about the level-of-detail in Tableau. Level-of-detail allows us to control the level of detail that is displayed in our charts and graphs.

Project Week
    Once students are done with all the fundamentals of data analytics, this data analytics essentials program will provide students with the third hands-on project on the topics learned so far.
Self-paced Module

Gain an understanding of what ChatGPT is and how it works, as well as delve into the implications of ChatGPT for work, business, and education. Additionally, learn about prompt engineering and how it can be used to fine-tune outputs for specific use cases.

Demystifying ChatGPT and Applications
  • Overview of ChatGPT and OpenAI
  • Timeline of NLP and Generative AI
  • Frameworks for understanding ChatGPT and Generative AI
  • Implications for work, business, and education
  • Output modalities and limitations
  • Business roles to leverage ChatGPT
  • Prompt engineering for fine-tuning outputs
  • Practical demonstration and bonus section on RLHF
Career support: Portfolio review and interview preparation sessions

The Data Analytics Essentials program from University of Texas at Austin and Great Learning assists you to showcase your portfolio and be on top of employer preferences with resume and Linkedin portfolio review sessions and interview preparation guidance. You can also add the projects worked on during the program to your portfolio and enhance your skill competency.

Certificate of completion from the University of Texas at Austin

Upon completion of the program, earn a certificate of completion from the University of Texas at Austin McCombs School of Business.

End of Data Analytics Essentials Program by UT Austin

Start of PL-300 Certification Training

This curriculum is optimally designed with the outcome to prepare you for the Microsoft Power BI Data Analyst PL-300 certification exam.

Working with Data in Power BI

    The outcome of this module is to learn how to navigate the power BI interface, connect with data, prepare it and create your 1st functional dashboard. Below are the topics covered in this week:

  • Understanding the Power BI interface and features
  • Creating visualizations and reports by connecting to data from various sources
  • Basic data modeling concepts and techniques
  • Data preparation, cleaning, and transformation using Power Query
  • Creating relationships between data tables
  • Advanced data modeling techniques
Creating Effective Visualizations

    The outcome of this module is to learn the best practices for creating visualizations, creating custom dashboards and visuals, and leveraging R and Python in PowerBI for developing advanced visualizations. Below are the topics covered this week:

  • Best practices for creating compelling and informative visualizations
  • Using custom visuals and formatting options
  • Creating dashboards to display multiple visualizations
  • Creating custom visuals using the Power BI Developer Tools
  • Using R and Python in Power BI for advanced visualizations and analytics
  • Building interactive, drill-down visualizations
DAX Basics & Advanced DAX

    The outcome of this module is to learn how to use DAX functions in PowerBI for various applications, working with tables and filters in DAX and optimizing DAX performance. Below are the topics covered this week:

  • Introduction to the Data Analysis Expressions (DAX) language
  • Using DAX functions to create calculated columns and measures
  • Understanding and using context in DAX calculations
  • Using advanced DAX functions for time intelligence, ranking, and more
  • Working with tables and filters in DAX expressions
  • Optimizing DAX performance
Productionizing Power BI reports

    The outcome of this module is to learn about deploying to PowerBI service, facilitating collaboration between individuals and groups, optimizing data models and queries, and handling large datasets. Below are the topics covered this week:

  • Deploying Power BI reports and dashboards to the Power BI Service
  • Sharing reports and dashboards with other users and groups
  • Using collaboration features like comments and notifications
  • Best practices for creating optimized data models in Power BI
  • Understanding query folding and optimizing queries for performance
  • Using DirectQuery and Live Connection for large datasets
Power BI Administration and Security

    The outcome of this module is to learn how to manage PowerBI entities, configure settings and permissions and leveraging PowerBI API for automation. Below are the topics covered in this week:

  • Managing Power BI workspaces, reports, and data sources
  • Configuring security settings and permissions in Power BI
  • Using the Power BI API for automation and integration
Project Week

    Work on a real-world project to apply the concepts studied and comprehend the applications of Power BI.

Hands-on Projects

Work on projects and implement your skills alongside established data experts and fellow learners from around the world.

Retail

FoodHub

Improving customer experience with EDA
A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app, the company has stored the data of the different orders made by the registered customers in their online portal. In this project, we will perform exploratory data analysis to analyze or get a fair idea about the demand of different restaurants and improve customer experience.
Tools & Concepts - Basics of Python, Pandas, Numpy, Preprocessing, Visualization(seaborn and matplotlib), Exploratory Data Analysis
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Automobile

New-Wheels

Analyzing after-sales feedback with SQL
New-Wheels, a vehicle resale company, has launched an app with an end-to-end service from listing the vehicle on the platform to shipping it to the customer's location. This app also captures the overall after-sales feedback given by the customer. In this project, we will query and manipulate the data using SQL database and answer the business questions and create a quarterly business report for the CEO of the company.
Tools & Concepts - MySQL Workbench, Databases, Fetching, Filtering, Aggregation, Joins, Window functions, Subqueries, Order of execution
Learn more

Entertainment

Movie Lens

Creating personalized dashboard with Tableau
MovieLens, an internet and entertainment company, offers an online database encompassing information on films, TV series, and streaming content, including details on cast, crew, trivia, ratings, and reviews. Annually, in collaboration with a guest curator, MovieLens releases insights centered around a theme, providing a comprehensive perspective. This year, the company plans to launch the "Movie Talkies: Classic" edition, aiming to engage viewers with specific movie preferences and promote classic films to attract new customers, thereby expanding their audience base.
Tools & Concepts - Implementing DAX commands, Data Modification and transformation, Setting Slicer and Navigator Actions, Filters and their Application
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Who is this program for?

Recruitment
Recruitment

Recent graduates with 0-3 years of experience looking to develop job-ready skills in Data Analytics and Business Intelligence domains.

Curated Jobs
Curated Jobs

People from across job functions looking to upskill and leverage data to make analytical decisions. This program prepares one for a role as a Data Analyst or a Business Analyst across industries.

Faculty and Industry Experts

You will learn new data analytics skills each week from esteemed UT Austin faculty and a global team of expert business analysts.

Our faculty

UT Austin

20+

Professors

UT Austin

2500+

Industry Mentors

UT Austin

2

Award winning faculties

Faculty
Dr. Dan Mitchell

Assistant Professor, McCombs School of Business

Faculty
Dr. Kumar Muthuraman

Faculty Director, Centre for Research and Analytics

Faculty
Mr. R Vivekanand

Operations Director

Faculty
Denver Dias

Senior Data Science Consultant

Faculty
Udit Mehrotra

Data Scientist

Great Learning Advantage

The program is distinguished by its unique combination of a comprehensive curriculum with a hands-on learning approach, interactive mentored learning, extensive program support, and career development assistance.

PERSONALISED AND INTERACTIVE

Live Mentored Learning in Small Groups

  • Weekend online mentorship from current Data Science practitioners
  • Small groups (up to 20 learners) for personalized learning
  • Live sessions with 2-way audio-video interaction
  • Complete hands-on exposure through ample projects
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STRUCTURED PROGRAM WITH GUIDANCE

Program Support and Networking

  • Dedicated Program Manager for academic & non-academic queries
  • Program Manager ensures that you stay on track and motivated
  • Interact with peers from diverse backgrounds during sessions
  • Grow your professional network and collaborate with peers

UNLOCK CAREER OPPORTUNITIES

GL Excelerate - Dedicated Career Support

  • Personalised career coaching and interview prep
  • Resume & LinkedIn review by experts

Career Success Stories

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Jonathan Sims

Student at John Brown University

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I'm a high school senior who took the Data Analytics Essentials course from UT McCombs School of business in collaboration with Great Learning. I learned MySQL, Excel, Python, Google Colab, Jupyter, and Tableau and feel confident in using them for future projects. This course has equipped me with all the essentials to pursue a career in Data Analytics. Thanks to UT and Great Learning for this opportunity.

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Lugina Qhespe

Contract Analyst at Abbott Laboratories

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I recently completed the Data Analytics Essentials Program with Great Learning and the University of Texas. I chose this program during the pandemic to improve my job prospects. The program taught me Pandas, Seaborn, and Google Colab, and I completed three projects in SQL, Python, and Tableau. These skills are already benefiting me in my current role, and I'm thankful for the Program Managers' support throughout the program.

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Vanysha Jackson

Music Curator for Apple

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I enrolled in the Data Analytics Essential Program by the University of Texas at Austin and Great Learning to use my analytical skills more as a music curator at Apple. My program manager and mentor were excellent, and I learned Excel, MySQL, Python, and Tableau. Now, I feel prepared for my journey as a data analyst. If you take this program, stay focused, show up, do the work, communicate, and ask many questions!

Program Fees

Program Fee: 2,900 USD*

Data Analytics Essentials Program: 2,000 USD

PL-300 Certification Training : 900 USD

Apply Now

*Great Learning delivers the PL-300 Certification training Program in collaboration with Microsoft. The University of Texas at Austin is not involved in the program's design or delivery.

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Pay in Installments

As low as 566 USD/month*

for 3 months

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Payment Partners

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*Subject to partner approval based on regions & eligibility. dLocal for Brazil, Colombia & Mexico learners. Other partners for U.S. learners only.

Benefits of learning from us

  • High-quality learning content from UT Austin & Global Faculty
  • Ace PL-300 certification and get Microsoft Power BI certified
  • 3 Hands-on Projects
  • Live Mentored Learning in Micro-classes (up to 15 learners)
  • Personalized Academic & Non-Academic Support
  • Career Support Services
  • Payable in 3 interest-free installments.

Application Process & Fees

1

Application

Register by completing the free online application form.

2

Screening Process

Your application will be reviewed to determine if the program is a good fit for you.

3

Payment

If selected, you will receive an offer for the upcoming cohort. Secure your seat by paying the program fee.

Upcoming Application Deadline

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Deadline: 30th Apr 2024

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Frequently Asked Questions

Program Details
Do I need to get a laptop, or will I be provided one?


All students are required to bring their own laptops. Nevertheless, once you enrol in the program, Great Learning will give you access to the necessary technology.

What tools and techniques will the PL-300 Microsoft Power BI certification course cover?


The course covers topics such as Power BI Desktop, Power Query, Data Modeling, Visualizations, DAX functions, and more. Additionally, learners will be exposed to best practices in Power BI Data Analytics, further preparing them for the PL-300 Microsoft Power BI Data Analyst exam.

Does the PL-300 Microsoft Power BI Data Analyst course include practice examinations?


Indeed. The course features practice exams that mirror the PL-300 Microsoft Power BI certification test. These practice exams are crucial in preparing learners for the actual Microsoft PL-300 certification exam.

What steps should I take to prepare for the PL-300 Microsoft Power BI Certification Exam?


The Data Analytics Essentials course provides comprehensive training for the Microsoft PL-300 exam. Learners will be thoroughly equipped for the Microsoft Power BI Data Analyst certification through guided online sessions, practical exercises, and dedicated support.

What job opportunities will become available after passing the Microsoft PL-300 Exam and obtaining the Certification?


By successfully completing the Microsoft PL-300 course and obtaining certification, learners will enhance their professional profiles, readying them for positions such as Data Analyst, Power BI Developer, Business Intelligence Analyst, and beyond.

What sets the Microsoft PL-300 course apart from other standard Data Analytics certification courses?


The PL-300 Microsoft Power BI Certification is distinct in its exclusive focus on Power BI tools, equipping learners for the PL-300 Microsoft Power BI Data Analyst exam. This unique emphasis distinguishes it from other generic Data Analytics certification courses.

Is the PL-300 Microsoft Power BI Data Analyst Certification suitable for beginners?


Certainly! This Data Analyst course for beginners offers modules expressly crafted to establish foundational skills, progressing toward a sophisticated understanding of Microsoft Power BI Data Analyst tools.

How can I become a Microsoft Certified Power BI Data Analyst Associate?


Learners will earn the Microsoft Power BI Data Analyst certification after successfully completing the Microsoft Power BI Certification Program and passing the PL-300 exam.

Who can apply for this Microsoft Power BI Certification Program?


All the learners enrolled in this Data Analytics course for beginners who want to advance in their careers and earn the Microsoft Power BI certification can enroll in this program. The program concentrates on Power BI and serves a broad audience interested in Power BI Data Analytics.

What is the PL-300 - Microsoft Power BI Data Analyst Certification Program?


This module is specialized to equip learners with the vital skills needed for data analysis and visualization. As part of the Microsoft Power BI certification, learners will gain practical experience using Power BI tools, enabling them to draw insights from data.

What is the Data Analytics Essentials course from the University of Texas at Austin’s McCombs School of Business?


The McCombs School of Business at the University of Texas at Austin (UT Austin) has collaborated with leading experts to design a world-class Data Analytics Essentials course. During the period of three months, learners will acquire the fundamental skills and expertise required to function in the contemporary analytical world.

How long is this Data Analytics online course for beginners?


The course lasts for 16 weeks. It is held in micro classes and taught by highly experienced industry experts.

How will I be assessed during the course?


The Data Analytics Essentials course is extensive, challenging, and continually evaluated. We evaluate a candidate's understanding of the subjects through quizzes, assignments, and projects.

What career opportunities will I receive after completing this Data Analytics course from UT Austin McCombs School of Business?

Upon completing this course, learners will get a wide variety of career opportunities. Some of the most in-demand jobs at entry-level in Data Analytics include the following:

 

  • Data Analyst

  • Business Analyst

  • BI Analyst

  • Data Journalist

  • Research Analyst

  • Product Analyst

  • Analytics Engineer

  • Data Architect

  • Data Engineer

What is the ranking of the UT Austin McCombs School of Business?


According to the QS World University Rankings 2021, the university is ranked 6th overall in the world for Business Analytics. According to the U.S. News & World Report, UT Austin has been consistently ranked among the top 20 public universities, thanks to its 40+ postgraduate programs and 15 undergraduate programs that are among the top 10 in the country.

What are the benefits of enrolling in this Data Analytics course for beginners from UT Austin McCombs School of Business?

The benefits of enrolling in this world-class Data Analytics course for beginners include the following:

 

  • The UT Austin Advantage: The McCombs School of Business at UT Austin is a reputable business school at a renowned public research university. UT Austin fosters ideas and cultivates principled leaders by providing top-notch instruction, valuable learning opportunities, and the pursuit of pertinent, ground-breaking research, educating those who will shape tomorrow and tackle the most challenging problems. Learners can rest assured that they are learning from the best of the best, thanks to a demonstrated track record of numerous successes, cutting-edge research, and teaching techniques.

 

  • Industry-relevant Curriculum: Several highly qualified faculty members from UT Austin’s McCombs School of Business developed the curriculum for this program. This comprehensive curriculum covers industry-relevant subjects, such as Excel, Python Programming, Descriptive Statistics, Data Analytics Foundations, SQL, Numpy, Pandas, Seaborn, Exploratory Data Analysis (EDA), and Data Visualization with Tableau.

 

  • Interactive Sessions: Through live interactive micro-classes, learners can connect with other peers. These micro-sessions are an incredible way to get them interested in the course materials and facilitate better conceptual understanding. 

 

  • Hands-on Learning: With hands-on learning, learners will gain in-depth knowledge of various critical concepts and discover how to apply them in the real world.

 

  • Prominent Faculty and Industry Experts: In order to give learners a practical understanding of essential concepts, the course brings together the distinguished faculty of UT Austin and a global team of highly skilled Data Analysts and Business Analysts.

 

  • Industry-relevant Projects: Learners will execute 3 hands-on projects spread across various modules. They will implement these projects alongside established data experts and fellow learners worldwide.

 

  • Live Online Mentoring Sessions: During these sessions, learners can speak with mentors from various fields. Additionally, they will gain assistance with projects and other critical concepts through live mentoring sessions.

 

  • Great Learning Advantage: Learners will gain access to dedicated career support from Great Learning throughout their educational journey, including career guidance sessions, resume reviews, LinkedIn Profile reviews, interview preparation, and e-portfolio.
     

  • Become Job-ready: The course will equip learners with theoretical knowledge and practical skills through case studies and hands-on projects. They will enhance their resume and secure career opportunities with Great Learning’s career support program. They’ll also get the opportunity to network with well-established industry experts, increasing their chances of succeeding in the Data Analytics field.

What is unique about this Data Analytics beginners course?

The Data Analytics beginners course is unique in the following aspects:

 

  • Through weekly one-on-one mentoring sessions and periodic real-world case studies, learners will gain knowledge and expertise from professional Data Analysts with various backgrounds.

  • The course vividly illustrates concepts pertaining to dynamic Data Analytics through expert-led sessions, self-paced videos, hands-on projects, and in-depth mentoring sessions.

  • Through the extensive curriculum, learners will acquire analytical skills that are in high demand across a wide range of industries, including healthcare, communication, business, management, and many others.

What is the Data Analytics online course syllabus for beginners?

The course syllabus has been developed to fulfil the necessities of recently graduated students and working professionals. This curriculum will discuss the following topics to help learners pursue fruitful careers in Data Analytics:

 

  • Prerequisites of Data Analytics: Microsoft Excel, Python Programming, Google Colab, and Descriptive Statistics.

 

  • Data Analytics Foundations: Overview of Industry 4.0 and the World of Data and Generating Insights using Excel.

 

  • Data Analytics with SQL: Querying Data with SQL and Advanced Querying to extract business insights.

 

  • Data-driven Insights using Python: Data Transformation using Numpy and Pandas, Data Visualization with Seaborn, and Exploratory Data Analysis.

 

  • Creative Storytelling with Tableau: Storyboarding 101 and Building Interactive Dashboards.

 

[Download the Brochure for a more detailed curriculum]

What are the learning outcomes of this online Data Analytics course from UT Austin McCombs School of Business?

The following are the learning outcomes of this online Data Analytics course:

 

  • Learners will understand the technical, conceptual, and business facets of the Data Analytics landscape.

  • They will be able to conduct Exploratory Data Analysis with Excel, Python, SQL, and Tableau.

  • They will query data in an SQL database in order to produce analytical data, business reports, and insights.

  • Learners will acquire the essentials of databases and learn the process of managing, extracting, and manipulating data with SQL.

  • They will be able to execute Data Visualization for storytelling to illustrate business problems.

  • They will gain familiarity with Python to analyze data and implement it in various business problems.

What languages, tools, and skills will I learn in this course?

Learners will get familiar with various in-demand languages, tools, and skills in this course, including:

 

  • Excel

  • Python

  • Business Statistics

  • Querying Data with SQL

  • RDBMS

  • NumPy

  • Pandas

  • Seaborn

  • Exploratory Data Analysis

  • Data Visualization using Tableau

Who will be the faculty for this Data Analytics for beginners online course?


The renowned and well-esteemed faculty members of UT Austin and highly qualified Business Analysts across the globe will teach this course and guide learners through the fundamentals of Data Analytics and its essentials.

What projects are included in this course?

Learners will execute 3 hands-on projects, which include the following:

 

  • Improving Customer Experience with EDA - FoodHub - Retail

  • Analyzing After-Sales Feedback with SQL - New-Wheels - Automobile

  • Creating a Personalized Dashboard with Tableau - Gamer’s Arena - Entertainment

What exactly does mentored learning involve?


The course teaches learners through a unique mentored learning approach, which takes place in a micro-class of 20-25 students. These live sessions include two-way voice and video communication.

What certificate will I receive after finishing this Data Analytics certificate course from UT Austin McCombs School of Business?


Students who finish the course successfully will be awarded a professional certificate in Data Analytics Essentials from the University of Texas at Austin.

What role does Great Learning play in this course?

Learners will receive career support assistance from Great Learning, a part of BYJU’s group and India's well-established ed-tech platform for higher education and professional development.

 

The following are the career support services offered by Great Learning:

 

  • Career Sessions: Learners will interact directly with industry experts to gain career insights.

  • Resume and LinkedIn Profile Review: The course will help learners put together an outstanding resume that highlights their abilities and previous employment experience.

  • Interview Preparation: Learners can gain an insider's perspective on what hiring managers are looking for and learn how to ace interviews with the help of interview preparation sessions.

  • E-Portfolio: Learners will discover how to build an industry-ready portfolio to showcase their proficiency with languages, tools, and skills.

Will I have access to the learning materials after finishing the course?


Yes. For three years after learners have completed the Data Analytics Essentials course, all lectures and learning materials will be available because learning should be never-ending.

Eligibility Criteria
What are the eligibility criteria for this online Data Analytics for beginners course?

The following are the requirements for this course's eligibility:

 

  • Enrollment in this course is open to recent graduates and early-career professionals who want to learn job-ready skills in the areas of Business Intelligence and Data Analytics. 

  • Candidates who want to dive into the world of data and use it to analyze decisions in various industries are eligible.

  • Candidates who desire to learn the fundamentals of Data Analytics quickly with a reasonable resource commitment can apply for this course.

Admission queries
When is the enrollment deadline for this Data Analytics course from UT Austin McCombs School of Business?


The admissions process is closed once the required number of people have signed up for the subsequent batch. The first-come, first-serve policy applies to the few seats available for this course. Apply before time to secure your seats.

What is the admission process to pursue this Data Analytics course for beginners?

To enroll in this course, the applicants must meet the eligibility criteria mentioned earlier. The admission process for the eligible candidates is provided below:

 

  • Step-1: Application Form

Register through a simple online application form.

 

  • Step-2: Screening Process

Attend a quick screening call to confirm that the course meets your objectives.

 

  • Step-3: Join the Program

Once selected, reserve your seat by paying the admission fee.

 

Note: Document verification is required before admission to the course.

Fee Related Queries
Does this course have a refund/cancellation policy?

 Please note that submitting the admission fee does constitute enrolling in the program, and the below cancellation penalties will be applied. If you are unable to attend your course, please review our dropout and refund policies below:

 

  1. A full refund can only be issued within 48 hours of enrollment.

  2. Admission Fee - If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded.

  3. The fee paid in excess of the admission fee:

    • Refund or dropout requests requested more than 4 weeks before the Commencement Date are eligible for a full refund of the amount paid in excess of the admission fee.

    • Refund or dropout requests requested more than 2 weeks before the Commencement Date are eligible for a 75% refund of the amount paid in excess of the admission fee.

    • Refund or dropout requests requested more than 24 hours before the Commencement Date are eligible for a 50% refund of the amount paid in excess of the admission fee.

    • Requests received after the Commencement Date are not eligible for a refund.

    • Cancellation must be requested in writing to the program office.

Are books or online learning resources subject to additional fees?


There are no additional fees because the Learning Management System (LMS) makes all the required learning materials available online

What is the fee to pursue this Data Analytics Essentials course from UT Austin McCombs School of Business?


The fee to pursue this Data Analytics course is USD 2700.

Cohort Start Dates

Great Learning Online

Online

11th May 2024

Still have queries?
Contact Us

Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at dae.utaustin@mygreatlearning.com or +1 512 877 8310.

Application Closes 30th Apr 2024

Download Brochure

Check out the program and fee details in our brochure

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Delivered in Collaboration with:

The University of Texas at Austin is collaborating with Great Learning to deliver Data Analytics Essentials. Great Learning is an ed-tech company that has empowered learners from over 170+ countries in achieving positive outcomes for their career growth.

The Market Trend for Data Analytics Essentials Courses

In today's data-driven world, the demand for professionals with Data Analytics skills is skyrocketing. With data being generated at an unprecedented rate, organizations across industries are looking to hire individuals who can make sense of this data and provide valuable insights to drive business decisions.

 

The demand for Data Analytics professionals is not limited to any industry. Businesses across sectors, including finance, healthcare, e-commerce, and marketing, seek individuals with Data Analytics skills. The demand for these professionals is expected to grow in the coming years. According to the U.S. Bureau of Labor Statistics, the employment of Data Analysts is projected to grow by 23% from 2021 to 2031, faster than the average for many other occupations.

 

To meet this massive demand, the University of Texas at Austin's McCombs School of Business has designed a world-class Data Analytics Essentials course. By pursuing the program, individuals can develop the skills and knowledge needed to meet the market demand for Data Analytics professionals. It provides a comprehensive understanding of Data Analytics tools and techniques, including Data Mining, Data Visualization, Statistical Analysis, and Microsoft Power BI.

 

Employers today are looking for individuals who can analyze data and communicate their findings effectively. This program addresses this need by teaching students to present data clearly and concisely. This skill is essential in today's business world, where decision-makers must quickly understand the insights gleaned from data analysis.

 

In conclusion, pursuing a Data Analytics Essentials course is an excellent way to meet the market demand for data analytics professionals. It gives students the skills and knowledge to analyze data, communicate insights effectively, and make data-driven decisions. With the need for Data Analytics professionals expected to proliferate in the coming years, now is the perfect time to pursue a career in this exciting field.

 

About Data Analytics Essentials Course from UT Austin McCombs School of Business

The Data Analytics Essentials program was created by seasoned professionals at the University of Texas at Austin (McCombs School of Business) to enable a thorough understanding of the field in 16 weeks. Learners will acquire crucial knowledge and abilities to become job-ready and succeed in the modern analytical world.

 

The curriculum is designed to allow Data Analytics enthusiasts to interact with and learn from highly qualified Data Analysts from various backgrounds, as well as weekly one-on-one mentoring sessions and case studies applicable to the real world. Through expert-led sessions, self-paced videos, three hands-on projects, and in-depth mentoring sessions, it vividly illustrates important data analytics concepts. Learners will acquire several in-demand analytical skills in various industries, such as communication, business, management, healthcare, and other fields.

 

The course also includes a 6-week PL-300 - Microsoft Power BI Data Analyst Certification Program. This module aims to equip participants with essential data analysis and visualization skills. Within the scope of the Microsoft Power BI certification, this module facilitates hands-on learning with Power BI tools to extract data insights.

 

Brief Description of UT Austin McCombs School of Business

The University of Texas at Austin was established in 1883 and currently enrolls more than 51,000 students and 3,000 teaching faculty. UT Austin is well-known on a global scale in the fields of social science, business, technology, and science.

 

The university is ranked 8th worldwide in Business Analytics by the QS World University Rankings 2023. In addition, UT Austin has consistently been ranked among the top 20 public universities by U.S. News & World Report because of its 40+ postgraduate programs and 15 undergraduate programs that are among the top 10 in the country.

 

Benefits of Learning Data Analytics Essentials from UT Austin McCombs School of Business

To ensure that students succeed in this program and are prepared for the workforce right out of the gate, the curriculum combines coursework directed by industry experts with assistance from highly qualified Data Analysts.

 

  1. Learning Content by World-Class Faculty

Learn from UT Austin's esteemed faculty members and a group of global leaders in Data Analytics.

 

  1. Live Mentorship Sessions with Industry Experts

Through weekly sessions, the program offers participants the chance to speak with highly knowledgeable mentors who can help them gain real-world experience. 

 

  1. Portfolio-Building Projects

In order to demonstrate their skills to prospective employers, learners will work on hands-on projects relevant to their industries.

 

Key Learning Outcomes of the Data Analytics Essentials Course

When learners complete this course, they will be able to:

 

  • Recognize the technical, conceptual, and commercial facets of the Data Analytics environment.

  • Learn Excel, Python, SQL, and Tableau to carry out Exploratory Data Analysis.

  • Query data in an SQL database to generate analytical data, business reports, and insights.

  • Learn the fundamentals of databases and how to manage, extract, and manipulate data using SQL.

  • Understand how to use data visualizations for storytelling to illuminate business issues.

  • Gain familiarity with Python for analyzing data in order to implement it in various business problems.

  • Ace the Microsoft PL-300 Certification exam.

 

More on PL-300 - Microsoft Power BI Data Analyst Certification Training Program

Power BI enables professionals to transform complex data into visually stunning representations, enabling clear and impactful communication of insights. This training program equips learners with all the essential skills to clear Microsoft’s PL-300 certification exam.

 

Highlights of the Microsoft Power BI Certification Program

 

  • 6-Week Online Program

  • Hands-on Learning and Academic Support

  • Live Virtual Classes with Microsoft Certified Instructors

  • Dedicated Program Manager

  • Exam Preparation Guide + Mock Exams

  • 50% Off on Exam Fee

 

Microsoft Power BI Certification

Learners will also be awarded a PL-300 - Microsoft Power BI Data Analyst Certification from Microsoft and Great Learning.

 

Job Opportunities After Learning Data Analytics Essentials

Learners will be qualified for a variety of entry-level technical job roles after completing the course, which includes the following:

 

  • Data Analyst: They gather, examine, and interpret data by determining trends, patterns, and connections in large data sets. They then use their newfound knowledge to inform business decisions.

 

  • Product Analyst: They collaborate closely with product managers and other key players to support the development of new products by conducting analyses of their specifications and market research.

 

  • Business Analyst: They analyze a company's data and processes to streamline its operations, create reports, and advise management on how the business can increase its overall effectiveness.

 

  • Analytics Engineer: They collaborate with Data Scientists to comprehend the business issues that must be resolved and choose the most effective techniques for gathering and processing business data.

 

  • Business Intelligence Analyst: They assist businesses in making wise decisions by creating business intelligence models and strategies that can result in higher profits and growth.

 

  • Data Architects: They develop and maintain data models, implement data management procedures, and establish data governance policies to design and implement an organization's data architecture.

 

  • Data Journalists: They search for and report on stories using various Data Analysis techniques, including statistical analysis, data visualization, etc. 

 

  • Data Engineer: They are responsible for the reliable and efficient data transfer within a firm. A Data Engineer collaborates with data architects to design data models and create ETL (extract, transform, load) processes for transferring data between systems.
     

  • Research Analyst: They gather information through primary and secondary research, examine data, and gain insights to aid decision-making. A Research Analyst collaborates closely with Data Scientists and Analysts to comprehend business needs and create research plans.