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

Program Overview

Certified Data Scientist Program is a 6 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

  •   180+ hours of learning
  •   Practical hands-on sessions
  •   20+ Case Studies and Assignments
  •   Globally Recognized Certification
  •   Live Sessions from Industry Experts
  •   100% Placement Assistance
  •   10+ hands-on Capstone Projects
  •   Lifetime access to self-paced learning
  •   3 months Live Internship
  •   24/7 Live Support
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

₹28,000 ($440)

for self-paced

₹42,000 ($620)

for self-paced and live sessions blended mode

₹52,000 ($760)

for classroom sessions
* EMI options available.

Tools Covered

Program 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 Projects

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Industry Projects

Learn by practicing real-life industry based projects on AI 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.

National Stock Exchange (NSE) Data Study

Processing National Stock Exchange (NSE) Data using Hive to find out various insights on the data and analyze the same.

NASA Kennedy Space Center logs

Churn the logs of NASA Kennedy Space Center server using Spark to find out useful business and devops metrics.

Netflix Movie Recommendation

Generate Netflix movie recommendations using Spark MLlib.

Analyzing Twitter Impacts

Analyze and derive the importance of various handles at Twitter using Spark GraphX.

Facebook image captioning

A model that can detect the type and objects in an image to classify facebook photos.

Stock Market trend Analysis

Predict any stock market equity shares trend using deep learning algorithms.

Certified Data Scientist



Get eligible for world-class certification thus adding that extra edge to your resume.

Admission Process

Step 1: Fill up the query form

Fill up the Query Form and one of our counselor will call you & understand your eligibility.

Step 2: Get Shortlisted & Receive a Call

Our Admissions Cell will review your profile. Upon qualifying, an email will be sent to you confirming your admission to the Program.

Step 3: Block your Seat & Begin the Course

Block your seat and begin your course and start your AI/ML journey!

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


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 Preparation

Dedicated mentorship and interview connect sessions are provided for cracking the interviews. Take mock interviews and get hired faster.

Resume Building

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

REVIEWS

What our learners have to say ...

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