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Professional Data Science Program

Data Science: The imminent frontier of technological advancement and innovation.

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online-education
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Live Training Hours
Online course
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Tools Covered

Program Prerequisites

No Prior Coding experience required.

Laptop with Internet Connections.

Basic Knowledge
of Maths.
Basic Communication
skills.

Introduction to Excel for Data Analytics

data-analytics
Weekends
  • Basics of Excel
  • Statistical and Mathematical Functions in Excel
  • Lookup functions, Index and Matching
  • Logical, Text, Datetime functions
  • Data Cleaning and feature engineering
  • What if analysis and Scenario Management
  • Charts and Dashboards
  • Linear Regression and Forecasting
Weekdays Plan
  • W1: Introduction to Maths for Data Analytics
  • W2: Introduction to Inferential Statistics
  • W3: Introduction to Power Query
  • W4: Introduction to Macros

Introduction to Tableau for Data visualization and BI

Weekends
  • Basics of Tableau
  • Tableau Charts and Maps
  • Table Calculations and Calculated fields
  • Aggregations, Granularity, LOD Expressions
  • Data Extracts and Filters
  • Tableau Dashboards and Storyboards
Weekdays Plan
  • W1: Data Preparation in Power BI
  • W2: Data Modelling in Power BI
  • W3: Dashboarding in Power BI
Data visualization

Introduction to Python

Weekends
  • Basics of Python
  • Introduction to Data Structures
  • Introduction to Functions in Python
  • Introduction to Strings and Regular Expressions
  • Loops and Conditionals
  • OOPs, and Space-Time Complexity
Weekdays Plan
  • W1: Fundamentals of R Programming
  • W2: Statistical Functions in R Programming
  • W3: Introduction to GGplot in R Programming

Introduction to Statistics and Hypothesis Testing

Weekends
  • Introduction to Descriptive Statistics
  • Introduction to Basic and Conditional Probability
  • Introduction to Inferential Statistics
  • Introduction to Hypothesis Testing
Weekdays Plan
  • W1: EDA, and Data Preparation in SAS
  • W2: Using SQL Queries in SAS
Statistics and Hypothesis

Introduction to Data Analysis and Visualization

Data Analysis
Weekends
  • Introduction to Numpy and Pandas
  • Basic Charts in Python
  • Advanced and Interactive Charts in Python
  • Case Study1 - Data Analysis and Visualization
Weekdays Plan
  • W1: Introduction to Query Analysis using Pandas
  • W2: Introduction to Plotly and Express

Introduction to Data Cleaning/Preparation/Processing

Weekends
  • Introduction to Missing Values and Outliers
  • Categorical Encoding Techniques
  • Data Transformations and Feature Engineering
  • Data Splitting, and Feature Scaling
  • Case Study2: Data Cleaning & Processing
Weekdays Plan
  • W1: Case Study1: Data Analysis and Visualization
  • W2: Case Study2: Data Cleaning and Processing
  • W3: Case Study3: Statistical Distributions
Data Cleaning

Introduction to Supervised Learning

Supervised-Learning
Weekends
  • Introduction to Linear Regression and Regularization
  • Introduction to Logistic Regression
  • Introduction to KNN, and SVM
  • Introduction to Decision Trees and Random Forests
  • Introduction to Boosting Algorithms
  • Introduction to Imbalanced Machine Learning
  • Advanced Modelling Techniques and Improvements
  • Project1: End-to-End ML Project. Dataset: Employee Attrition Dataset.
Weekdays Plan
  • W1: Introduction to Regularization Techniques
  • W2: Implementation of Regression Algorithms in Real-world Datasets
  • W3: Implementation of Classification Algorithms in Real-world Datasets
  • W4: Advanced Modelling Techniques.

Introduction to Unsupervised Learning

Weekends
  • Introduction to K Means and Hierarchical Clustering
  • Introduction to PCA and LDA
  • Project2: Clustering Analysis Dataset: Mall Customers segmentation Dataset
  • Casestudy3: Dimensionality Reduction
Weekdays Plan
  • W1: Evaluation Metrics for Unsupervised Learning
  • W2: Case Study 4: Dimensionality Reduction
Unsupervised Learning

Introduction to Time series forecasting and Recommender systems

forecasting-and-Recommender
Weekends
  • Time Series Fundamentals
  • Introduction to AR, MA, ARMA Models
  • Introduction to ARIMA and SARIMA
  • Introduction to ARIMAX and SARIMAX
  • Project3: Time Series Forecasting Dataset: Sales Forecasting Dataset
  • Project4: Recommendation Engine Dataset: Movie Recommendation Dataset
Weekdays Plan
  • W1: Implementation of Time Series algorithms on Real-world Datasets
  • W2: Implementation of Recommender Systems on Real-world Datasets

Introduction to Big Data and SQL

Weekends
  • Database Fundamentals
  • DQL, DDL, and DML Queries
  • SQL Joins
  • SQL sub-queries and set-operations
Weekdays Plan
  • W1: Fundamentals of MongoDB: Documents and Collections
  • W2: MongoDB: Replica sets, Sharding, and Indexes
big_data

Program flow

6 Months

Learn Python, R, Tableau, Excel, SQL, Power BI, Statistics, Data Science and Machine Learning with real-world Projects and Case-studies.

1 Month

Become Job ready by preparing a Project Portfolio on Linkedin, Github, Hackerrank, Medium etc.

Program Outcomes

  • Depth understanding of R and Python Programming Language.
  • Ability to create solutions for data science problems using Python or R.
  • Depth understanding of Power BI and Tableau for BI.
  • Depth understanding of Databases and SQL.
  • Ability to perform Advanced Query Analysis on Databases.
  • Depth understanding of Advanced Excel for Data Analytics.
  • Ability to create Data Driven Dashboards using Excel/PowerBI/Tableau.
  • Depth understanding of Statistics and Hypothesis Testing.
  • Ability to work on any kind of Text/Image/Audio/Video/numerical Datasets.
  • Ability to perform Exploratory Data Analysis on any Dataset.
  • Ability to Clean, Process, and Prepare any dataset for Predictive Modelling.
  • Great understanding of Machine Learning, and Deep Learning Algorithms.
  • Ability to work with real-world dataset and apply ML/DL algorithms.
  • Great understanding of Natural Language Processing Concepts and Algorithms.
  • Ability to perform Time series forecasting using Time series Models.
  • Ability to create Recommender systems using content or collaborative filtering.
  • Depth understanding of Unsupervised Machine Learning algorithms.
  • Ability to perform Dimensionality Reduction on High dimensionality datasets.
  • Ability to perform Clustering analysis on any kind of datasets.

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