Learn Data Science using Python and R Programming through Industry Projects & kick start your career in Data Analytics
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What you will learn in the Data Scientist Training?

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Who should take this Data Science Online Training Course?

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What are the prerequisites for learning Data Science?

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What are the Data Science job opportunities in Mumbai?

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What is the Data Science market trend in Mumbai?

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

- 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

Histogram

Normal Distribution

Skewness, Kurtosis

Outlier detection

5. Inferential Statistics

6. Hypothesis Testing

7. Analysis of variance (ANOVA)

Two sample t-Test

F-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

Association

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

Classification

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

Clustering

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

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

- 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

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

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.

“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.

- 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

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