Data Analytics Essentials With PL-300- Microsoft Power BI Data Analyst Certification Training Program 16 Weeks Expert online Instruction Certificate of Completion from The University of Texas at Austin Download Brochure Apply Now Application Closes Tomorrow Enquire: +1 512 877 8310 With 6-Week Microsoft Power BI Data Analyst Certification Training Program In Collaboration with: #3 MS - Business Analytics QS World University Rankings, 2022 #6 Executive Education - Custom Programs Financial Times, 2022 Why choose data analytics 11.5 million new data science and analytics jobs will be created by the year 2026 Source: Analytics Insight 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 Read More 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 Read More 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 Read More Learn from world-renowned faculty from University of Texas at Austin Acquire in-demand skills in programming and tools for data analytics Get a hands-on learning experience with real world projects and cases Be job ready with career guidance and a certificate of completion Learn More Data Analytics Essentials Learn to leverage data and upskill in 16 Weeks Download Brochure Certificate from The 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. KUMAR MUTHURAMAN Faculty Director, PGP-DSBA H. Timothy (Tim) Harkins Centennial Professor Faculty Director, Center for Research and Analytics MS & PhD: Stanford University For any feedback & queries regarding the program, please reach out to us at MSB-DSBA@mccombs.utexas.edu GL Excelerate - Career Support 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. Career Sessions Interact personally with industry professionals to get valuable insights and guidance. Resume & LinkedIn Profile Review Present yourself in the best light through assets that truly showcase your strengths. Interview Preparation Get an insiders’ perspective to understand what recruiters look for. e-Portfolio Build an industry-ready portfolio to showcase your mastery of skills and tools.
Prepare to get Microsoft Certified Ace PL-300 certification with Data Analytics Essentials Program Prepare for PL-300 Certification Exam with: Live Virtual Classes with Microsoft Certified Instructors Hands-on Learning and Academic Support Exam Preparation Guide + Mock Exams Free Exam Voucher 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. Download Curriculum 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. MySQL Installation Here, students will understand the process of installing MySQL (a free and open-source database management system) on their systems. 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. PL-300 Microsoft Power BI Data Analyst 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 Exam Preparation Guide + Mock Exams The outcome of this module is to review key concepts and skills, and prepare for the PowerBI certification exam, through practice questions and Mock exams. Below are the topics covered in this week: Review of key concepts and skills for the PL-300 certification exam Practice exam questions and discussion of strategies for success Tips for preparing and taking the exam Key skills: Exam Preparation Practice Exams Study Strategies 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 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. 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. 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 offers access to multiple restaurants through a single 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 the demand of different restaurants and improve customer experience. Tools & Concepts - Python, Pandas, Numpy, Seaborn Learn more 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 captures the overall after-sales feedback given by the customer. In this project, we will create a pipeline to organize and maintain the data using SQL database, answer the business questions, and create a quarterly business report for the company's CEO. Tools & Concepts - MySQL, Databases, DML Learn more Entertainment Gamer’s Arena Creating personalized dashboard with Tableau Gamers' Arena is a website that provides information about video games. It keeps track of the games that have been released and their sales across various platforms and genres. In this project, we must design an interactive dashboard and make necessary decisions based on the insights generated by answering the business questions. Tools & Concepts - Tableau Learn more Who is this program for? Recent graduates with 0-3 years of experience looking to develop job-ready skills in Data Analytics and Business Intelligence domains. 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 Prof. Dan Mitchell Clinical Assistant Professor The University of Texas at Austin Dr. Kumar Muthuraman Faculty Director, Centre for Research and Analytics McCombs School of Business, University of Texas at Austin Mr. R Vivekanand Operations Director Wilson Consulting Private Limited Denver Dias Senior Data Science Consultant S P Jain School of Global Management Udit Mehrotra Data Scientist Dell Technologies 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 View Experience 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 Jonathan Sims Student at John Brown University 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. Lugina Qhespe Contract Analyst at Abbott Laboratories 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. Vanysha Jackson Music Curator for Apple 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 Fees: 2,700 USD Apply Now Pay in Installments As low as 566 USD/month* for 3 months View All Installment Plans 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. × InstallmentPlans Full fee payment plan Admission Fees: USD 500 Monthly Installment Installments EMI / Per Month Installment 1 USD 566 Installment 2 USD 567 Installment 3 USD 567 Total Fee Payment 2200 USD Application Process 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 Admissions are closed once the requisite number of participants enroll for the upcoming cohort . Apply early to secure your seat. Deadline: Tomorrow Apply Now Reach out to us We hope you had a good experience with us. If you haven’t received a satisfactory response to your queries or have any other issue to address, please email us at help@mygreatlearning.com
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