The module covers the basics of programming for non-programmers. Learn programming concepts and implement those concepts to write efficient code. Get introduced to Lists and OOPS concepts. What will you achieve?

• Build a solid foundation for programming
• Practice coding skills with 20+ coding questions

Topics

• Introduction to Programming
• Variables & Arithmetic Expressions
• Functions
• Data Types
• Conditions & Conditional statements
• Lists
• OOPS

The module covers the basics of Excel for data science. Learn Excel essentials to get a strong hold on using Excel for data analysis. Get introduced to pivot tables, formulae and charts. What will you achieve?

• Build a strong foundation of Excel for data analysis
• Summarise data with pivot tables and charts

Topics

• Excel Basics
• Excel Essentials
• Formulae
• Data Analysis
• Charts
• Pivot Tables
• Lookup

The module introduces learners to SQL programming. Learn how to query data from databases and create datasets for data analysis. What will you achieve?

• Build a strong foundation of SQL for data querying
• Create datasets for data analysis

Topics

• DDL, DML, DQL statements
• Aggregate, Date Functions
• Union, Union All & Intersect Operators
• Joins
• Views & Indexes
• Sub-queries

The module introduces the learners to Python programming. Work with multiple Python data science libraries to execute essential tasks like mathematical calculations, data manipulation, etc. Learn data visualisation with Python. What will you achieve?

• Master Python programming
• Run data analysis process using Python libraries
• Create useful charts for data visualisation

Topics

• Python Objects, List
• Functions
• Numpy
• Pandas
• Data Frame Manipulation
• Data Visualization
• EDA

The module introduces the learners to statistics. Review, analyse, and draw conclusions from data. Apply quantified mathematical models to appropriate variables for data analysis. What will you achieve?

• Build a strong statistics foundation
• Analyse data and draw conclusions using statistics
• Implement mathematical models for data analysis

Topics

• Introduction to Statistics
• Probability Theory
• Probability Distributions
• Hypothesis Testing
• Statistical Tests

The module introduces the learners to machine learning and explains how different ML algorithms work. It also covers the optimisation of models and model tuning. Get a complete understanding of how organisations are applying ML to solve their problems and grow the business. What will you achieve?

• Learn machine learning algorithms & their applications
• Analyse data and make predictions
• Solve real-world business problems using ML

Topics

• Introduction to Machine Learning
• Types of Machine Learning
• Linear Regression
• Optimization Techniques
• Gradient Descent
• Logistic Regression
• Model tuning
• Decision Trees
• Random Forests
• K-Means CLustering
• Hierarchical Clustering
• Principal Components Analysis (PCA)
• Time Series

The module covers data visualisation with two popular business intelligence tools - Tableau and Power BI. Create charts to articulate data and present insights to the business. Learn to showcase a data-based story to the stakeholders. What will you achieve?

• Master data visualisation with Tableau
• Master data visualisation with Power BI
• Create meaningful dashboards for the business

Topics

• Introduction to Tableau
• Tableau Interface and Chart Types
• Visual Analytics with Tableau
• Dashboard and Stories
• Power BI Interface
• Data Transformation
• Data Modelling
• Visual Data Analytics with Power BI

This module focuses on preparing the learners for placement based on their capability and performance during the course. Work on multiple projects with Project Mentor to sharpen your ML skills. Learn advanced ML concepts and AI fundamentals to enhance your profile. What will you achieve?

• Sharpen ML skills through projects
• Learn advanced ML skills
• Get introduced to AI

Topics

• ML Algorithms - Ensemble, Bagging, Boosting, KNN, SVM
• Advanced ML - Naïve Bayes, Recommendation Engine
• Introduction to Neural Network and AI
• Computer Vision
• NLP and Text Analytics

This module focuses on preparing the learner for placement based on their capabilities and performance during the course. Work on multiple projects with a Project Mentor to sharpen your data analytics and ML skills. Learn big data analytics with Hadoop & Spark to enhance your profile. What will you achieve?

• Sharpen ML skills through projects
• Learn big data analytics with Hadoop
• Execute machine learning with Spark

Topics

• ML Algorithms - SVM, Association Rules
• Practice Projects on ML
• Big Data & Hadoop Framework
• Machine Learning with Spark
• Advanced SQL

Build a data science project on your own by applying the learning from the bootcamps. Learn planning the project and implementing it successfully. Present your project to a team of evaluators from the industry and get valuable feedback. Add the project to you GitHub project portfolio. What will you achieve?

• Learn to plan a data science project
• Solve a real-world problem using data science
• Gain confidence on your skills by presenting the project to industry experts

Skills

• Combination of all skills learned throughout the course
• Project Presentation Skills

Prepare for interview opportunities by polishing the key skills. Get support to create your digital profile and resume. Sharpen your interview skills through interview preparation workshops and expert-based mock interviews. Resolve your career related queries through a mentorship session. At the end of the module, you will be ready for real interviews. What will you achieve?

• Build an exciting resume and digital profile for recruiters
• Sharpen your interview skills with mock interview sessions
• Resolve any career-related queries to have clarity and confidence

Skills

• Resume Building
• GitHub Project Portfolio
• Interview Preparation Workshops
• Mock Interviews
• Career Mentorship