Upskill & Transition to Data Analyst Role

A 3-6 month live structured program designed by industry experts to upskill & successfully transition to Data Analyst roles.

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From This Program, You Will Gain

All the Right Skills required to transition to Data Analyst roles

Multiple Industry-Relevant Projects to showcase in your resume

Career Transition to a top-tier company as a Data Analyst

Structured Curriculum for a smooth transition to Data Analysis Roles

Module 1 : Data Fundamentals
duration_icon Duration: 6 Weeks

The first step towards becoming a Data Analyst is to have a strong command over the fundamentals of reporting, storing & retrieving data.

Within this module, our goal is to become confident in data fundamentals.

Topics that will be covered:

  1. Excel

    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google spreadsheets

  2. SQL

    • Introduction to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering & Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes & Partitioning

  3. Beginner Python

    • Flowcharts, Data Types, Operations
    • Conditional Statements & Loops
    • Strings
    • In-build Data Structures – List, Tuples, Dictionary, Set
    • Matrix Algebra, Number Systems
Module 2 - Data Analysis and Visualization
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Duration: 6 Weeks

As a Data Analyst, it is important we know how to break down business situations, design correct metrics & visualize the data.

Within this module, you will learn all of this.

Topics that will be covered:

  1. Python Libraries

    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scrapping
    • Beautifulsoup
    • Tweepy

  2. Probability & Applied Statistics

    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central Limit Theorem
    • Hypothesis Test, AB Testing
    • ANOVA
    • Correlation
    • EDA Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK OpenCV

  3. Tableau/ PowerBI

    • Visual Analytics
    • Charts, Graphs, Operations on Data & Calculations in Tableau/ PowerBl
    • Advanced Visual Analytics & Level of Detail (LOD) Expressions
    • Geographic Advanced Charts, and Worksheet & Workbook
    • Formatting
 
Module 3 - Fundamentals of Machine Learning & Deep Learning
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Duration: 8 Weeks

The field of data analysis is rapidly evolving with the integration of machine learning. Having a foundation in these areas ensures that data analysts remain competitive and relevant in the job market.

Within this module, you will gain a solid foundation of Machine Learning.

Topics that will be covered:

  1. Advanced Python

    • Python Refresher
    • Basics of Time & Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handlin & Modules

  2. Math for Machine Learning

    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient Descent
    • Principal Component Analysis

  3. Introduction to Neural Networks & Machine Learning

    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perception and Softmax Classification
    • Introduction to Clustering, K-Means
    • K-Means, +4, Hierarchical
Module 4 - Getting Hired

Once you have upskilled yourself to become a great data analyst, it is important that we now focus on getting you interview opportunities from diverse companies.

This process is usually in 3 phases:

1. Build a strong profile

2. Applying the right way

3. Acing the interview

We focus on all the above 3 objects in this phase.

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