Special Offer: Minimum 50% off on all courses till 25 May 2020

Course Overview

Certified Data Scientist Program is a 3 months intensive job-oriented learning program designed to help you gain a robust understanding of Data Science. Through this Data Science certification program, you will gain knowledge in Python, Data analysis, Statistics, Data visualization, SQL, Machine Learning, Natural Language Processing, Web Scraping, Tableau, etc. On successful course completion, you will master the tools on Data Science and you will be confident enough to solve any real time data science problems.


Key Highlights of the program

  • 3 months intensive training
  • Practical hands-on sessions
  • 20+ hands-on projects + 1 Capstone
  • One-on-One doubt clarification
  • Classroom as well as Live Online Classes
  • Lifetime access to self-paced learning

Course Advantage

Learning - Blended to Perfection

Solve real-world problems with hands on practicals via Industry based projects and detailed personalised feedback.

Personalized Mentorship

Receive guidance from Industry experts with one-on-one feedback on submissions of assignments.

Career Assistance

Build you resume, prepare for mock interviews and setup online portfolio with placement updates.

Eligibility

The program is aimed at candidates who:
  • has either completed or pursuing a Bachelor's degree with a minimum of 50% aggregate marks or equivalent.
  • are comfortable using a programming language
  • are familiar with college-level mathematics and statistics

Curriculum

  • Introduction to Data Science
  • Introduction to Data Mining
  • Roles of a Data Scientist
  • Selection Bias
  • Introduction to various Data Formats
  • Data Science models, tools and packages
  • Applications of Data Science
  • Introduction to Python
  • Python Environment setup and Python essentials
  • Python Fundamentals
  • Datatypes with Python
  • Basic Operators in Python
  • Decision Making and Loops
  • Functions and Classes
  • Quizes and Assignments
  • Introduction to Python tools for Data Science
  • Mathematical Computation using Numpy
  • Numpy Array Computations
  • Numpy In-Depth
  • Scientific Computing using SciPy
  • SciPy sub package
  • SciPy In-Depth
  • Data Manipulations using Pandas
  • Series and DataFrame operations using Pandas
  • Data Pre-processing using Pandas
  • Pandas SQL operations
  • Quizes and Assignments
  • Introduction to Visualization in Python
  • Different types of graphs - walkthrough
  • Matplotlib Graphical Visualization
  • Seaborn for Data Visualization
  • Quizes and Projects
  • Introduction to Database Systems
  • SQL Introduction
  • Programming in SQL
  • Connecting to Databases
  • Data preparation using SQL
  • Pandas SQL operations
  • Quizes and Projects
  • Introduction to Excel
  • Data Analysis using Excel Functions
  • Data Visualization in Excel
  • Quizes and Projects
  • Fundaments of Statistical Analysis
  • Basic Terminologies in Statistics
  • Branches of Statistics
  • Variables
  • Quantitative vs Qualitative
  • Statistical Case Studies
  • Quizes and Assignments
  • Sampling Data with and without replacement
  • Sampling Methods, Random vs Non-Random
  • Measurement on Samples
  • Random Sampling methods
  • Various other Sampling methods
  • Biased vs Unbiased Sampling
  • Sampling Error
  • Quizes and Assignments
  • Measures of Central Tendencies
  • Measures of Dispersion
  • Percentiles
  • Empirical Rule
  • Scoring
  • Outliers
  • Quizes and Assignments
  • Introduction to Distributions
  • Normal Distribution
  • Central Limit Theorem
  • Normalization
  • Bernoulli and Poisson Distributions, etc
  • Normality Testing
  • Skewness
  • Kurtosis
  • Measure of Distance
  • Euclidean, Manhattan and Minkowski
  • Quizes and Assignments
  • Hypothesis Testing
  • Null and Alternate/Research Hypothesis
  • P-value
  • Two tailed, Left tailed
  • Type I and Type II error
  • Parametric vs Non-Parametric Testing
  • T-Test : One sample, Two sample, Paired
  • Introduction to ANOVA
  • One way ANOVA
  • Two way ANOVA
  • Non Parametric Test : Chi-Square test and Wilcoxon Signed Rank, etc
  • Quizes and Projects
  • Types of Correlation
  • Weak and Strong Correlation
  • Correlation Analysis
  • Quizes and Assignments
  • Introduction to Regression
  • Type of Regression
  • Cost Function or Loss function
  • Univariate Linear Regression
  • Multiple Linear Regression
  • Categorical Data Analysis
  • Logistic Regression
  • Quizes and Projects
  • Introduction to Machine Learning
  • Applications of Machine Learning
  • Supervised vs Unsupervised
  • Batch vs Online
  • Modelling ML
  • Machine Learning Algorithms
  • Quizes and Assignments
  • Overfitting and Underfitting
  • Train Test Split
  • K fold cross validation
  • Quizes and Assignments
  • Regression Evaluation
  • Confusion Matrix
  • Precision and Recall
  • ROC Curve
  • F1 score
  • Quizes and Assignments
  • Univariate Linear Regression
  • Multivariate Linear Regression
  • Linear Discriminant Analysis
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Learning Vector Quantization
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier
  • Quizes and Projects
  • K-Means Clustering
  • Association Rule Learning
  • Apriori Algorithm
  • Quizes and Projects
  • Decision Trees
  • Random Forest
  • Adaboost
  • Gradient Boost
  • XG Boost
  • Quizes and Projects
  • Introduction to Time Series Analysis
  • Exploratory Time Series Data Analysis
  • Correlation and Autocorrelation
  • Autoregressive (AR) Models
  • Moving Average (MA) and ARMA Models
  • Quizes and Projects
  • Key Concepts of Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Quizes and Projects
  • Introduction to Text Analytics and NLP
  • NLTK in Python
  • Tokenization
  • Text Cleaning
  • Stemming and Lemmatization
  • Sentiment Analysis
  • Quizes and Projects
  • Introduction to Deep Learning
  • Introduction to Perceptrons
  • Introduction to Artificial Neural Networks
  • Training Deep Neural Networks
  • Up and Running with TensorFlow
  • TensorFlow Basics
  • Single Layer Perceptron
  • Multiple Layer Perceptron
  • Deep Learning using Keras
  • Creating an Artificial Neural Networks(ANN)
  • Convolutional Neural Networks(CNN)
  • Recurrent Neural Networks(RNN)
  • Autoencoders
  • Quizes and Assignments
  • Tableau Interface
  • Dimensions and measures
  • Filter shelf
  • Distributing and publishing
  • Connecting to Data Sources
  • Extracting and Interpreting data
  • Plots with Super Store data
  • Time Series Data
  • Forecasting
  • Quizes and Assignments
  • Final Capstone Project

Tools Covered

Industry Projects

Learn by practicing real-life industry based projects on Data Science sponsored by top companies across industries.

Benefits

  • Engage in collaborative projects with student-mentor interaction
  • Learn and solve problems in-guidance with expert mentors
  • Get regular feedbacks on the projects and learn to improve your skills.

Walmart Recruiting – Store Sales Forecasting

Modeling retail data is the need to make decisions based on limited history. The holiday markdown events are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

IMDb Movie Analysis

Information regarding different movies were downloaded with the purpose of creating a movie recommendation app. The data was preprocessed and cleaned to be ready for machine learning applications.

Recommender System

While browsing an ecommerce website, users are always overwhelmed by multiple choices. A recommender system helps user to make the right decision which in turn increases customer satisfaction as well as company sales.

Uber Supply-Demand Gap

The aim of the analysis is to identify the root cause of cancellation and non-availability of cabs and recommend ways to improve the situation.

Cancer Patient Diagnosis

Cancer prediction models provides a magnificent approach to assessing the risk and diagnosis by identifying patients at high risk. Predict the risk factor associated with cancer patients.

Barclays Fraud Detection

Identifying credit risk and fraud detection. Investigate suspicious activity and overcome credit risk using Machine Learning Algorithms.

Certification Programs

We will be providing certificate after completion of the course and based on the course credits.

Associate Certification Program

Associate Certification Programs are job-oriented training programs designed to help you gain a robust understanding of fundamental concepts with real time projects.

Master Certification Program

Master Certification Programs are designed to get in-depth knowledge. You will learn advanced new concepts and skills which will make you an expert on the area of study.

Assisted Learning Formats

In each format, course content and modules will remain same. It gives more flexibility for learning to almost anyone, regardless of their scheduling commitments.

Classroom Training


  • For students preferring online classes
  • Live interaction with mentors for doubt resolutions
  • Get recorded videos of the missed sessions
  • Classes on either weekdays or weekends
  • Maximum 10 Students in a batch

Online Live Training


  • For students preferring classroom training
  • Individual personalized doubt resolution with expert mentors
  • Get recorded videos of the missed sessions
  • Classes on either weekdays or weekends
  • Maximum 20 Students in a batch

Facts that Make us Unique

Expert Mentors

Receive high quality guidance from industry experts and teaching assistants on the respective courses. Get one-on-one feedback on submissions of projects.

Industry case Studies

Work on real time industry problems via sponsored projects with detailed personalised feedback and guidance from the mentors.

Interview preparation

Practice mock tests and mock interviews and get familiarized with commonly asked interview questions that will help you crack any technical interview. Use your ePortfolio created with the guidance of our instructors to showcase your skills and improve your chances of getting hired.

Lifetime Content Access

Get lifetime access to self-paced learning materials.

Doubt Resolution

Timely doubt resolution by peers and mentors. Get personalised expert feedback on assignments and projects.

Student Support

Get 24x7 support related to any queries or technical issues. Get personalized support from the mentors on task related issues.

Career and Placement Support

With 100% Placement Assisstance from us, get ready for the job market by taking mock interviews, resume preparation and real time projects and assignments.

Get Hired Faster

Get regular notifications on job openings and interviews from our placement team until you get placed in a company. Get mentorship and guidance for interview preparation.

Hands on Projects

Our trainig methodology is completely practical based. Learn by working on real time projects and case studies from Uber, Amazon, Walmart labs, etc.

Interview Scheduling

Our placement team will be sending your updated resume to the recruiters and consultants till you find a suitable position in a company.

Resume Building

Our training and placement team will help you out in building your resume with real time projects as per the industry standards.

Watch the Intro

Why learn Data Science?

Data is the essence of all industries. Large and humongous amount of data is being generated every day, which is termed as Big data. Without the expertise of professionals who can turn this huge amount of data using cutting-edge technology to actionable insights, this Big Data is not useful. Today, more and more organizations are using big data and increasing the value of a data scientist who knows how to work around, analyse and find out actionable insights from this data. More and more businesses today are using Data Science to add value to every aspect of their operations. This has led to a substantial increase in the demand for Data Scientists.

Data Science is the core of Artificial Intelligence. Without the right use of data, there is no intelligence. Data is the lifeblood of the digital enterprise, Artificial Intelligence (AI) technology is the pumping heart of the digital enterprise. AI, especially its subsets including machine learning, deep learning and advanced analytics, can automate much of the insight gathering and decision making in a data-driven enterprise, and amplify the value of data many times over.

Some Job Titles Related to Data Science:


  • Data Scientist
  • Data Engineer
  • Data Analyst
  • Product Analyst
  • Data Architect
  • Big Data Engineer
  • Business Analytics Specialist
  • Data Visualization Developer
  • Data Science Engineer
  • Business Intelligence (BI) Engineer
  • BI Solutions Architect
  • BI Specialist
  • Machine Learning Engineer
  • Deep Learning Engineer
  • Decision Scientist
  • Analytics Manager
  • Statistician

Reasons to study Data Science now:


What our learners have to say ...

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