Learn Data Science using Python and R Programming through Industry Projects & kick start your career in Data Analytics
  • Data Science Modules: Python, R, Statistics, Hadoop, Machine Learning, Artificial Intelligence, Tableau & Deep Learning
  • Work on Real Projects, Build a Portfolio, Attend Interviews and Get Hired.
  • No. 1 training Institute in Mumbai offering classroom training on Data Scientist
  • Certified Data Science Trainer with 8+ Years Real Industry Experience
  • Best faculty with Excellent Lab Infrastructure along with detailed course material.
  • We Prepare your CV/Resume to attend Interviews and securing a Job.
  • Trained over 10470+ professionals in Data Science with Real Time Projects
  • We Share Common Interview FAQs, Interview Handling Skills
  • One-to-one Attention by Instrutors
  • 1. Introduction to Data Science.
    a. What is data science? How Data Science differ from BI and Reporting
    b. Who are data scientist? What skill sets are required?
    c. What are roles in a data science in project?
    d. What is Analytics and Predictive Analytics?
    e. Challenges in Using Predictive Analytics.
  • 1. Setting Up the Problem
    a. Stages of a data science project.
    b. Setting Expectation.
    c. Overview with case study
    1. Business Statistics
    a. Data types
    Continuous Variable
    Ordinary Variable
    Categorical Variables
    a. Descriptive Statics
    b. Inferential Statics
    c. Types of data
    d. Population and sampling
    e. Measure of Central Tendency
    f. Measure of Variability
    g. Histogram
    h. Box Plot
    i. Skewness
    j. Kurtosis
    k. Random variable
    l. Probability Distribution
    m. PDF and CDF of Random Variable
    n. Expected Value of Mean
    o. Variance and Standard Deviation
    p. Data Distribution.(Normal Distribution, Binomial Distribution and Gaussian distribution)
    q. Z score, Standard Normal Distribution
    r. Statistical Inference
    s. Sampling : Types of Sampling
    t. Simple Random Sampling, Stratified Sampling
    u. Probability and non-probability Sampling
    v. Central Limit Theorem
    w. Point Estimation and Interval Estimation
    x. Confident Interval and Interpreting CI
    y. Confident Interval for the mean
    z. T-distribution and its characteristics
    a. Estimate Error with t-distribution
    b. When to use z or t distribution
    c. Hypothesis Testing
    Testing Process
    Type I error
    Type II error
    Null and Alternative hypothesis
    Reject or acceptance Criteria
    a. ANOVA
    b. F-distribution
    c. Statistical test for equality of mean
    d. Interview questions
  • 1. R Programming
    a. What is R?
    b. R Data Types in R
    c. Types of objects in R – Lists, matrices, arrays, data. Frames etc.
    d. Creating new variables or updating existing variables
    e. Variable declaration.
    f. String Manipulations
    g. Writing Function in R
    h. Conditional Statement
    i. Loops and summaries
    j. Importing and Exporting
    k. Data Manipulation and Transformation
    l. Merging datasets
    m. Efficient Data handling in R
    n. Interview Question
    1. Exploratory Data analysis and Visualization.
    a. Getting data into R – reading from files
    b. Cleaning and preparing the data – converting data types (characters to numeric)
    c. Importance of EDA in Data Science.
    d. Exploring Numerical Data
    e. Numerical Summary
    f. Correlation vs causation
    g. Case study and interview question
    1. Predictive Analytics
    a. Supervised Learning
    b. Simple Linear Regression.
    c. Assumptions
    d. Sum of least squares
    e. Model validation
    f. Error Measurement – RMSE – Root Mean Square Error
    g. Disadvantage of linear Models
    h. Multiple Linear regression.
    i. Multivariate Linear regression.
    j. Interpretation using R Programming
    k. Accuracy measurement
    l. Visualization using ggplot2
    m. Case Study with Interview questions
  • 1. Classification
    a. What a Decision Tree Is
    b. How to create a Tree
    c. Classification and Regression Tree.
    d. Algorithms ID3, C4.5, C5.0 with R-Implementation
    e. What can go wrong? (Over fitting)
    f. Accuracy measurement.
    g. Misclassification
    h. Area under the Curve
    i. Interview questions
    1. Re-Sampling and Ensembles Methods
    Random Forest algorithm with R-Implementation
    Bagging and Boosting – Gradient Boosting Method
    1. Logistic Regression
    a. What is Logistic Regression?
    b. Need for logistic Regression
    c. Logistic Regression Implication using R Programming
    d. Interpreting Logistic Regression Model
    e. Accuracy measurement
    f. Other Practical Consideration for Logistic regression model
    g. Interview questions
    1. KNN – K nearest neighbors
    Advantages and disadvantages
    1. Un-Supervised learning
    a. Hierarchical Clustering with implementation in R
    b. K-Means Clustering with implementation in R
    c. Interview Questions
    1. Probalistic Methods
    Naive Bayes
    1. Forecasting Algorithm
    a. Time series analysis using ARIMA model.
    b. Case study
    c. Interview questions.
    1. Building Web application with shiny.
    a. Shiny review
    b. Make the perfect plot using shiny
    c. Explore a dataset interactively with shiny
    d. Interview Questions.
    1. Model Deployment and Cross validation
    a. Need Cross Validation and k –fold cross validation
    b. Model Deployment
  • 1. Advance Methods
    Support Vector Machine
    Neural Networks
    Introduction of Deep Learning
    1. Working in Cloud machine Leaning Tools
    Introduction of Azure ML
    Introduction to AWS ML
    1. Project implementation in real data
  • Data Science with Python(10-12 Weeks)
  • 1. Python for Data Science
    2. Introduction to Statistics
    Types of Statistics
    Analytics Methodology and Problem Solving Framework
    Populations and samples
    Parameter and Statistics
    Uses of variable: Dependent and Independent variable
    Types of Variable: Continuous and categorical variable
  • 3. Descriptive Statistics
    4. Picturing your Data
    Normal Distribution
    Skewness, Kurtosis
    Outlier detection
    5. Inferential Statistics
    6. Hypothesis Testing
    7. Analysis of variance (ANOVA)
    Two sample t-Test
    One-way ANOVA
    ANOVA hypothesis
    ANOVA Model
    Two way ANOVA
  • 8. Regression
  • Exploratory data analysis
    Hypothesis testing for correlation
    Outliers, Types of Relationship, Scatter plot
    Missing Value Imputation
    Simple Linear Regression Model
    Multiple Regression
    Model Building and Evaluation
    9. Model post fitting for Inference
    Examining Residuals
    Regression Assumptions
    Identifying Influential Observations
    Detecting Collinearity
    10. Categorical Data Analysis
    Describing categorical Data
    One way frequency tables
    Cross Tabulation Tables
    Test of Association
    Logistic Regression
    Model Building
    Multiple Logistic Regression and Interpretation
  • 11. Model Building and scoring for Prediction
    Introduction to predictive modeling
    Building predictive model
  • Scoring Predictive Model
    Introduction to Machine Learning and Analytics
  • 12. Introduction to Machine Learning
    What is Machine Learning?
    Fundamental of Machine Learning
    Key Concepts and an example of ML
    Supervised Learning
    Unsupervised Learning
    13. Linear Regression with one variable
    Model Representation
    Cost Function
    Parameter Learning
    Gradient Descent
    14. Linear Regression with Multiple Variable
    Computing parameter analytically
    Ridge, Lasso, Polynomial Regression
    15. Logistic Regression
    Hypothesis Testing
    Decision Boundary
    Cost Function and Optimization
    16. Multiclass Classification
    17. Regularization
    Overfitting, Under fitting
    18. K-Nearest Neighbor – Classification and Regression
    19. Support Vector Machine
    20. Introduction to Naïve Bayes, Random Forest
    21.Model Evaluation and Selection
    Confusion Matrix
    Precision-recall and ROC curve
    Regression Evaluation
    22. Unsupervised Learning
    K-mean Algorithm
    23. Dimensionality Reduction
  • Principal Component Analysis and applications
  • 24. Introduction to text analytics
    25. Introduction to Neural Network
    27. Problems with Data Analytics Project

What you will learn in the Data Scientist Training?

  1. Understanding the various roles and responsibilities of Data Scientist
  2. Data analysis, project lifecycle and data science in real world
  3. Master the techniques of evaluation, experimentation and project deployment
  4. Work with machine learning algorithms
  5. Analysis segmentation using clustering and the technique of prediction
  6. Learning how to integrate R with Hadoop
  7. The various steps in the installation of Impala
  8. Data science projects, analytics and recommender systems.

Who should take this Data Science Online Training Course?

  • Machine Learning professionals
  • Predictive Analytics & Information Architects
  • Big Data Experts, BI & Analyst professionals
  • Big Data Statisticians
  • Machine Learning professionals
  • Predictive Analytics & Information Architects
  • Those looking for Data Science career

What are the prerequisites for learning Data Science?

There are no particular prerequisites for this Training Course. If you love mathematics, it is helpful.

What are the Data Science job opportunities in Mumbai?

Mumbai has to the top IT companies and head office all gloable marketplayers and data science is really booming in India. There are top MNCs and start-ups alike that are looking for Data Scientists with the right skills in order to decipher all that data and convert it into valuable insights. Companies like IBM, Accenture, Infosys, EMC are ready to pay top salaries in order to get the right candidates.Enrolling with Intellipaat training institute for classroom training in Mumbai for Data Science can help you master this domain.

What is the Data Science market trend in Mumbai?

“By end of 2018, India alone will face a shortage of close to 2 Lakh Data Scientists” as quoted by the Indian National Daily The Hindu. Since the amount of Data is increasing with each passing day, and as Mumbai is rightly the IT capital and also the back-office of the world it means the market trend for Data Science domain in Mumbai is really heating up. Due to this there is a strong demand supply mismatch which can help the trained and certified Data Science professionals.

Why should you take the Online Data Scientist Course in Mumbai?

  • Data Scientist is the best job of the 21st century - Harvard Business Review
  • Global Big Data market to reach $122B in revenue by 2025 – Frost & Sullivan
  • The US alone could face a shortage of 1.4 -1.9 million Big Data Analysts by 2018 – Mckinsey
Data scientist is the best job of the 21st century as quoted by the Harvard Business Review. So due to this there is a premium attached to the role of a Data Scientist. The role of a Data Scientist is central to the working of today’s data-driven organizations. There is an urgent need to convert data into insights and be the bridge between the technical and non-technical departments and take high level business decisions which is where the role of Data Scientist gains increased importance.Intellipaat Data Science training in Mumbai can help you get the Data Scientist Certification and grab the best jobs.

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