• Assured data-analytics job opportunities
• 200+ hours of learning
• No cost EMI options available
• Fullstack Academy live training
• AI powered profile builder
• Career mentorship sessions (1:1)
• Just-in-time interviews
• Designed for working professionals
• Tableau and AWS Certification preparation included
• Certificate programme from Caltech
• Caltech approved faculty masterclasses
• Student Support available all day for your convenience (24*7)
• Interview preparation
• 5 out of 6 learners get positive career growth

Top Skills You Will Learn

Excel, SQL, Powerpoint, Tableau, AWS, Python, NLP, Artificial Intelligence, Machine Learning

Who Is This Programme For?

Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals

Minimum Eligibility

1. English language skills, computer skills
2. Programming experience not mandatory
3. The program design and curriculum assumes as undergraduate degree has been earned by the learner. However, undergraduates/freshers from STEM discipline can also apply.

Job Opportunities

Data Analyst, Product Analyst, Business Analyst, Marketing Intelligence Analyst, Business Intelligence Analyst

Syllabus covered

Prep Course

• Data Analysis in Excel + Presentation

1. Simple and Advanced Functions in Excel, Pivot Table, Vlookup and PowerPoint Skills

• Business Problem Solving

1. Approaching open-ended real-world problems using data as a lever to draw actionable insights.

Python Bootcamp

1. Welcome Module (to be released on the day of the webinar with a deadline of 1 day)
2. Introduction to Python

Course 1 - Python for Data Science

• Programming in Python + Python Graded Questions

1. Data Structures: Lists, Strings, Dictionaries, and Stacks, Time Complexity, Searching and Sorting, Two Pointers and Recursion

• Python for Data Science

1. Numpy & Pandas: Array, Vectors, Getting & Cleaning Data

• Python for Data Science

1. Numpy & Pandas: Array, Vectors, Getting & Cleaning Data

• Data Visualization in Python

1. Univariate, Bivariate, categorical time series plots using Seaborn and Matplotlib

• EDA

1. Uni & Mutivariate Analysis, Derived Metrics, Skewness, Outliers and Normalization

• Python Assignment

1. Reinforce the concepts learnt in data science through this rigorous assignment involving the past hundred years of movie data.

Course 2 - SQL

• Database Design and Introduction to MySQL

1. Stacks, Queues, Trees, Complexity, OLAP/ OLTP, RDBMS and SQL Querying

• Advanced SQL

1. Stacks, Queues, Trees, Complexity, OLAP/ OLTP, RDBMS and SQL Querying

• Data Modelling

1. DDL, ERD, DML, Database Creation, Querying & Manipulation

• Advanced SQL II

1. Stored Routines, Cursors, Query Optimization and Window Functions

• SQL Assignment

1. Use SQL skills to derive inferences to help a production house with relevant recommendations.

Course 3 - Tableau

• Tableau Certification

1. Data Analysis, Types of Charts and Visualisations on Tableau Interface and Dashboard Making. Get ready to master the fundamentals required for Tableau Certification

• Data Storytelling

1. Components of Data Storytelling, Narrative, 5 Patterns of Insights, Pyramid Principle

• Tableau Assignment

Course 4 - Machine Learning

• Inferential Statistics

1. Discrete & Continuous Distributions and Central Limit Theorem

• Hypothesis Testing

1. Null & Alternate Hypothesis, P - value, Critical Value, A/B Testing

• Linear Regression

1. Best Fit Line, Cost Function, Gradient Descent, Residual Sum of Squares, Residual Analysis and Prediction

• Logistic Regression

1. Binary classification, Sigmoid function, Likelihood function, Odds and Log Odds, Confusion Matrix, Accuracy, Sensitivity, Specivity and ROC Curve

• Unsupervised Learning: Clustering

1. K- Means, Hierchichal clustering, K - Mode and Association Rules

• Decision Trees

1. Homogeneity, Gini Index, Entropy and Information Gain, Tree Truncation and Pruning and Choosing Hyperparameters

• Time Series Forecasting

1. Basic Components of Time Series Forecasting, Simple Average and Naive Forecast Methods and Smoothing Techniques

• Assignment

Course 5 - AWS

• AWS Certification

1. Understand Data Storage solutions, Analysis, modelling and visualization techniques through AWS Glacier, Glue, Athena, Redshift and more to be capable of giving AWS certification examination.

Capstone Project

• Capstone

1. Bring your own capstone project that can give you an understanding of data sourcing, cleaning, analysis and modeling techniques. Work on Storytelling principles of your capstone.

• Optional

1. Random Forest
2. Boosting
3. Advanced Regression
4. Introduction to Deep Learning - Artificial Neural Networks, Backpropagation & TensorFlow
5. Convolutional Neural Networks
6. Deep Learning Assignment
7. Introduction to Natural Language Processing - Lexical Processing
8. Syntactic Processing
9. NLP Assignment
10. Advanced Time Series
11. Domain Specific Modules - BFSI/Healthcare
12. Interview Prep Content