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Data Analytics Essentials

Data Analytics Essentials

Master data analytics applications and secure a future-ready career

Application closes 27th Feb 2025

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Program Outcomes

Advance your career in Data Analytics

Become a data analytics expert

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    Understand data analytics from business, technical, and conceptual aspects

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    Analyze and visualize data using Excel, SQL, Python, and Tableau

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    Query and manage databases using SQL to generate insights and reports

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    Solve business problems with data storytelling and end-to-end Python analytics

Earn a certificate from UT Austin

  • U.S. News & World Report, 2024

    #7 Public University in the U.S.

    U.S. News & World Report, 2024

  • ranking 4

    #4 in MS - Business Analytics

    QS World University rankings, 2023

  • ranking 6

    #6 in Executive Education - Custom Programs

    Financial Times, 2022

  • U.S. News & World Report, 2022

    #7 Business Analytics (In USA)

    U.S. News & World Report, 2022

Key program highlights

Why choose the data analytics essentials program?

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    Learn from world’s top university

    Earn a certificate from a world-renowned university, taught by the esteemed faculty of UT Austin

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    Industry-ready curriculum

    Learn the foundations of Python, GenAI, Data Visualization and more from top UT Austin faculty. Gain valuable insights and apply your skills.

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    Learn at your convenience

    Gain access to content online, including lectures, assignments, and live webinars which you can access anytime, anywhere

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    3 hands-on projects

    Work on projects alongside established data scientists and fellow learners worldwide

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    Get expert mentorship

    Interact with mentors specialised in Data Analytics and get guidance to complete and showcase your projects

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    Personalized program support

    Get 1:1 assistance from a Program Manager. Access GL Community, project discussion forums and peer groups

Skills you will learn

Data Analysis with Excel

Descriptive Statistics

Python Programming

Data Transformation with Pandas and Numpy

Exploratory Data Analysis (EDA)

Data Visualization with Seaborn and Tableau

Interactive Dashboard Creation

PowerBI

Generative AI and ChatGPT Applications

Storytelling with Data

SQL Querying and Data Management

Data Analysis with Excel

Descriptive Statistics

Python Programming

Data Transformation with Pandas and Numpy

Exploratory Data Analysis (EDA)

Data Visualization with Seaborn and Tableau

Interactive Dashboard Creation

PowerBI

Generative AI and ChatGPT Applications

Storytelling with Data

SQL Querying and Data Management

view more

Secure top data analytics jobs

  • 11.5 million

    new jobs by 2026

  • Up to $110K

    avg annual salary

  • $105 million

    mkt value by 2027

  • 92%

    orgs use data

Careers in Data Analytics

Here are the ideal job roles in Data Analytics:

  • Data Analyst

  • Product Analyst

  • Business Analyst

  • Analytics Engineer

  • BI Analyst

  • Data Architect

  • Data Engineer

  • Data Journalist

  • Research Analyst

  • Data Scientist

Our alumni work at top companies

  • Overview
  • Career Transitions
  • Why GL
  • Learning Journey
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Reviews
  • Career support
  • Fees
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This program is ideal for

The Data Analytics Essential certificate program is designed for professionals looking to develop essential data analytics skills. It equips participants with the ability to interpret data, generate insights, and apply data-driven solutions in their careers or businesses.

  • Professionals Seeking Data-Driven Insights

    Designed for those looking to use data for strategic decision-making and business impact.

  • Career Changers Moving Into Analytics

    Suitable for individuals transitioning into data-focused roles such as data or business analysts.

  • Entrepreneurs & Consultants

    Helps business owners and consultants integrate data analytics into their strategies.

  • Aspiring Data Enthusiasts

    Suited for professionals looking to develop foundational data analytics skills for career growth.

Upskill with one of the best data analytics programs

  • UT Austin Programs

    Other Courses

  • Certificate

    hands upCertificate from UT Austin

    hands downNo university certificate

  • Gen AI modules

    hands upExtensive coverage of Gen AI topics

    hands downLimited coverage

  • Live mentored learning

    hands upLive interactive online classes with industry professionals 

    hands downLimited to no live classes

  • Career support

    hands upYes, with ePortfolio and profile review

    hands downNo career support

  • Hands-on projects

    hands up3 industry projects

    hands downFewer projects

  • Dedicated Program support

    hands upDedicated support to complete your course

    hands downLimited support

Experience a unique learning journey

Our pedagogy is designed to ensure career growth and transformation

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    Learn with self-paced videos

    Learn critical concepts from video lectures by UT Austin faculty and data analytics experts

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    Engage with your mentors

    Clarify your doubts and gain practical skills during the weekend mentorship sessions

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    Work on hands-on projects

    Work on industry-guided projects and apply the concepts & tools to solve business problems

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    Get personalized assistance

    Our dedicated program managers will support you whenever you need

Get an exclusive free preview of the course

Explore faculty videos and mentorship sessions. Get insights into relevant case-studies and projects.

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Ace Power BI Certification

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Ace PL-300 certification*

Prepare for PL-300 Certification Exam with:

  • Live Virtual Classes with Microsoft Certified Instructors

  • Hands-on Learning and Academic Support

  • Exam Preparation Guide

  • 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.

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

Enhancing Query Proficiency

Subqueries allow one to nest queries within other queries, enabling more complex and flexible data manipulation. This module will equip participants with advanced techniques for filtering data based on conditional expressions or calculating derived values to extract and manipulate data dynamically.

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.

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.

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.

Generative AI

This course provides you with an overview of what ChatGPT is and how it works, business applications of ChatGPT, and an overview of other generative AI models/tools via demonstrations 

  • ChatGPT - Overview 
  • ChatGPT - Business Applications 
  • Generative AI Demonstrations

Data Visualization using Tableau

In this course, you will learn how to read, explore, and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards. 

  •  Storytelling with Data 
  • Creating Interactive Dashboards

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 and 5.0 Continuing Education Units (CEUs)

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.

Prepare Data for Analysis with PowerBI

This module aims to equip participants with the skills to prepare data from various sources to harness the potential of Power BI for data analysis and visualization. Participants will start by exploring the Power BI interface and its features, learn how to connect to data from various sources, get introduced to basic data modeling concepts followed by Power Query for data preparation, and explore advanced data modeling techniques to get the data ready for analysis and deriving actionable insights from their data. 

 (PowerBI interface and features, Connecting to data from various sources, Creating visualizations and reports, Basic data modeling, Power Query for data preparation, cleaning, and transformation, Creating relationships between data tables, Advanced data modeling techniques)

Model Data with PowerBI

This module delves into data modeling techniques within Power BI, equipping participants with the skills to design and implement efficient data models. Participants will explore various data modeling approaches in Power BI, explore designing data models using the star schema and other relevant data structures, utilize DAX (Data Analysis Expressions) commands to create sophisticated calculations and measures, and build comprehensive reports and visualizations using queries and analytical techniques, helping participants transform raw data into actionable insights and compelling visual narratives. 

 (Data modeling approaches in PowerBI, Designing data models using star schema and data structures, DAX commands for creating calculations and measures, Building reports and visualizations using queries and analytics)

Advanced Data Modeling with PowerBI

This module focuses on advanced techniques in Power BI to enhance data analysis and model management. Participants will explore advanced time intelligence calculations using DAX functions, implement strategies for optimizing performance in Power BI models and reports, improve query performance with DirectQuery and variables, and gain insights into securing data access and controlling model objects, ensuring data integrity and compliance with organizational policies. 

 (Advanced time intelligence calculations using DAX functions, Optimizing performance in PowerBI models and reports, Improving query performance with DirectQuery and variables, Managing data complexity with cardinality reduction and aggregations, Securing data access and controlling model objects)

Build PowerBI Visuals and Reports

TThis module equips participants with the skills to design and create impactful reports that effectively communicate insights and engage users. Participants will cover the key principles involved in understanding user needs and design reports that address those needs for clear and effective communication. They’ll also gain insights into designing and customizing report layouts and visualizations to present data in the most meaningful way, use report objects and filtering techniques to enhance the functionality and interactivity of their reports, and learn to enable user interaction and real-time data exploration, allowing users to dive deeper into the data and extract valuable insights on the fly. 

(Understanding user needs and designing reports for effective communication, Creating engaging and informative reports with a focus on user experience, Designing and customizing report layouts and visualizations, Using report objects and filtering techniques to enhance report functionality, Enabling user interaction and real-time data exploration)


Building Advanced Visuals and Reports Using PowerBI

This module is tailored to enhance participants' skills in designing and creating advanced reports using various techniques and best practices. Participants will learn how to design detailed, interactive reports emphasizing the importance of visual storytelling, explore methods to optimize report performance and usability, and learn to create reports tailored for specific use cases, such as mobile devices and paginated reports, ensuring versatility and adaptability in various contexts. 

(Designing detailed and interactive reports for in-depth analysis, Creating engaging and informative reports with visual storytelling, Optimizing report performance and usability, Analyzing and visualizing data with advanced techniques, Creating reports for specific use cases, such as mobile devices and paginated reports)


Manage Workspaces and Datasets in PowerBI

 This module provides participants with the knowledge and skills needed to effectively deploy and manage Power BI solutions within an organization. Participants will learn best practices for securing and protecting Power BI data and models, techniques for monitoring and troubleshooting Power BI performance and issues, implementing row-level security and data governance to control and manage data access, and creating and managing dashboards for data exploration and insights, empowering them to design interactive and insightful dashboards that facilitate informed decision-making and data-driven exploration. 

(Deploying and managing PowerBI solutions, Securing and protecting PowerBI data and models, Monitoring and troubleshooting PowerBI performance and issues, Implementing row-level security and data governance, Creating and managing dashboards for data exploration and insights)

Hands-on learning & projects

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

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    learners group

  • 2-way

    audio-video interaction

  • 3

    real-world projects

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Retail

Improving customer experience with EDA

About the Project

Analyze customer order data to understand restaurant demand and enhance user experience through exploratory data analysis.

Skills you will learn

  • Python basics
  • Data manipulation with Pandas and NumPy
  • Data visualization using Seaborn/Matplotlib
  • Exploratory Data Analysis
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Automobile

Analyzing after-sales feedback with SQL

About the Project

Query and manipulate SQL database to generate business insights and create a quarterly report for decision-making.

Skills you will learn

  • SQL querying and filtering
  • Aggregation and joins
  • Subqueries and window functions
  • Report generation for business insights
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Entertainment

Creating personalized dashboard with PowerBI

About the Project

Analyze viewer preferences and promote classic movies to expand the customer base for an online entertainment database

Skills you will learn

  • Data Visualization
  • PowerBI Filters and DAX Commands
  • PowerBI Calculations
  • PowerBI Slicer and Navigator Actions
  • PowerBI Filter Actions

Master in-demand data analytics tools

Learn relevant skills, tools, and concepts

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    Python

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    Excel

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    RDBMS

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    Business statistics

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    Tableau

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    powerBI

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    SQL

  • And More...

Earn a Professional Certificate from UT Austin

Get a certificate from one of the top universities in USA and showcase it to your network

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* Image for illustration only. Certificate subject to change.

Meet your faculty

Learn new data analytics skills each week from esteemed UT Austin faculty and expert business analysts

  • Dr. Daniel A Mitchell - Faculty Director

    Dr. Daniel A Mitchell

    Clinical Assistant Professor, Department of Information, Risk & Operations Management, McCombs School of Business, The University of Texas at Austin

    Research Director, Center for Analytics and Transformative Technologies

    15+ years of experience in financial engineering and quantitative finance.

    Know More
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  • Dr. Kumar Muthuraman - Faculty Director

    Dr. Kumar Muthuraman

    Professor, McCombs School of Business, UT Austin

    Faculty Director, Center for Analytics and Transformative Technologies

    21+ years' experience in AI, ML, Deep Learning, and NLP.

    Know More
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  • Mr. R Vivekanand - Faculty Director

    Mr. R Vivekanand

    Co-Founder and Director

    Expert in data visualization and marketing econometrics with 10+ years

    Qualified Tableau trainer passionate about teaching business analytics

    Know More
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  • Denver Dias - Faculty Director

    Denver Dias

    Senior Data Science Consultant

    Holds 8+ yrs exp. & delivered AI solutions for Fortune 500 firms

    Expert in A/B testing, ML models, and predictive analytics

    Know More
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  • Mr. Udit Mehrotra - Faculty Director

    Mr. Udit Mehrotra

    Data Scientist, Stripe

    10+ years of experience in data science

    Former Data Scientist at Mc.Kinsey & Company, Dell

    Know More
    Stripe Logo

Watch inspiring success stories

  • learner image
    Watch story

    "The SQL and Python course enhanced my ability to extract practical insights from data"

    The program offered practical exercises in data analysis and programming. Professors were highly skilled, and the focus on SQL and Python greatly improved my technical abilities.

    Ashenafi Terefe

    Medical Technologist , USF

  • learner image
    Watch story

    "Hands-on projects and 1:1 sessions helped me build confidence in SQL, Python, and data visualization"

    The program offered a solid foundation in SQL, Python, and data visualization. The hands-on projects were highly effective in reinforcing learning, and the 1:1 sessions ensured I had the support I needed.

    Alexa Martin

    Senior HR DOT Compliance Analyst , DHL

Get dedicated career support

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    1:1 career sessions

    Interact personally with industry professionals to get valuable insights and guidance

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    Interview preparation

    Get an insiders perspective to understand what recruiters are looking for

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    Resume & Profile review

    Get your resume and LinkedIn profile reviewed by our experts to highlight your skills & projects

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    E-portfolio

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

Course fees

The program fee is 2,900 USD

Data Analytics Essentials Program: USD 2000

PL-300 Certification Training Program: USD 900

Invest in your career

  • benifits-icon

    Understand data analytics from business, technical, and conceptual aspects

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    Analyze and visualize data using Excel, SQL, Python, and Tableau

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    Query and manage databases using SQL to generate insights and reports

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    Solve business problems with data storytelling and end-to-end Python analytics

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Easy payment plans

Avail our flexible payment options & get financial assistance

  • INSTALLMENT PLANS

    Upto 3 months Installment plans

    Explore our flexible payment plans

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Payment Partners

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*Subject to partner approval based on applicable regions & eligibility

Take the next step

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Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 27th Feb 2025

Application Closes: 27th Feb 2025

Talk to our advisor for offers & course details

Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

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    1. Fill application form

    Apply by filling a simple online application form.

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    2. Interview Process

    Go through a screening call with the Admission Director’s office.

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    3. Join program

    Selected candidates will receive an offer letter. Secure your seat by paying the admission fee.

Batch Start Date

Got more questions? Talk to us

Connect with a program advisor and get your queries resolved

Speak with our expert +1 512 877 8310 or email to dae.utaustin@mygreatlearning.com

career guidance

Best Data Analytics Course for Beginners - The University of Texas at Austin (UT Austin)
 

Faculty at the University of Texas at Austin (McCombs School of Business) have designed the Data Analytics Essentials program to facilitate a comprehensive understanding of the field in 16 weeks. Learners will gain the critical skills and expertise required to thrive in the contemporary analytical world.

 

The program is crafted to enable data analytics enthusiasts to interact and learn from highly skilled data analysts with diverse backgrounds in weekly one-on-one mentorship sessions and work on industry-relevant real-world case studies. It brings potent data analytics concepts to life through expert-led sessions, self-paced videos, three hands-on projects, and in-depth mentorship sessions. Learners will acquire the most in-demand analytical skills across various industries, including communication, business, management, healthcare, and several other sectors.

 

The program also comprises a 6-week PL-300 - Microsoft Power BI Data Analyst Certification Program. This module is designed to provide learners with the necessary data analysis and visualization skills. As part of the Microsoft Power BI certification, the module offers practical experience using Power BI tools for deriving insights from data.

 

The UT Austin Advantage in this Data Analytics Program for Beginners

The University of Texas at Austin was founded in 1883 and now enrolls more than 51,000 students and 3,000 teaching faculty. UT Austin is known across the world as a pioneer in the fields of social science, business, technology, and science.

 

According to the QS World University Rankings 2023, the university is ranked 8th across the globe in business analytics. UT Austin has also continuously been ranked among the top 20 public universities by U.S. News & World Report, thanks to its 40+ postgraduate programs and 15 undergraduate programs that are among the top 10 in the United States.

 

Benefits of Learning the Fundamentals of Data Analytics from UT Austin

The curriculum combines coursework directed by expert Faculty in collaboration with professional data analysts to ensure that learners succeed in this program and are job-ready. 

 

  1. Recorded Lectures by World-Class Faculty

Learn from world-renowned faculty from UT Austin and a team of international data analytics experts.

 

  1. Mentorship Sessions with Industry Experts

Through weekly sessions, the program allows learners to interact with highly knowledgeable mentors who can assist them in gaining practical experience.

 

  1. Portfolio-Building Projects

Learners will work on hands-on projects, tests, and case studies that are pertinent to their industries and showcase their abilities to potential employers.

 

Key Learning Outcomes of the Data Analytics Program for Beginners

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

 

  • Understand the technical, conceptual, and business aspects of the data analytics landscape.

  • Conduct exploratory data analysis using Excel, Python, SQL, and Tableau.

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

  • Acquire the fundamentals of databases and learn how to use them for managing, extracting, and manipulating data with SQL.

  • Learn how to use data visualizations for storytelling to enlighten business issues.

  • Gain familiarity with Python for analyzing data to implement in a variety of 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 intricate data into visually stunning representations, enabling clear and impactful communication of insights. This training program equips learners with all the necessary 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 Mastering Data Analytics Essentials

Upon completing the course, learners may be eligible for several entry-level technical job roles, including:

 

  • Data Analyst: Data Analysts collect, analyze, and interpret data by identifying trends, patterns, and relationships in data sets and gain insights to help businesses make better decisions.

 

  • Product Analyst: Product Analysts work closely with product managers and other stakeholders to support the product development process by performing analysis of product requirements and market data.

 

  • Business Analyst: Business Analysts analyze the data and procedures of a company to help streamline its operations, develop reports, and make recommendations to the administration on how the company can improve its overall efficiency.

 

  • Analytics Engineer: Analytics Engineers work with data scientists to understand the business problems that need to be solved and identify the best ways to collect and analyze data for businesses.

 

  • Business Intelligence Analyst: Business Intelligence Analysts help companies make informed decisions by developing business intelligence models and plans that can lead to increased profits and growth.

 

  • Data Architect: Data Architects design and implement an organization’s data architecture, including the development and maintenance of data models, data management processes, and data governance policies.

 

  • Data Journalist: Data Journalists use various data analysis techniques, such as statistical analysis, data visualization, etc., to uncover and report stories they discover.

 

  • Data Engineer: Data Engineers are responsible for the reliable and efficient movement of data within an organization. A data engineer works with data architects to design data models and build ETL (extract, transform, load) processes for moving data between systems.

 

  • Research Analyst: Research Analysts conduct primary and secondary research, analyze data, and develop insights to support decision-making. A research analyst works closely with data scientists and business analysts to understand business needs and design research plans.

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