**Data Science Course Training**

SocialPrachar Provides Best **Data Science Course Training **with Certified Trainers. Data Science Course is in Big Demand now with #1 Place in National and International Job Market. We also Provide Data Science Classroom Training **in Hyderabad** and Data Science online Training for rest of the world audience. Register now for our data science 3 months exclusive training program includes Training on Advanced data science course which includes Machine Learning, Statistics, R Program, Python, Linear Regression, Tableau Etc.

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## Data Science course Classroom Training available in Hyderabad

Classroom Training is provided for a duration of 2 months on Saturdays and Sundays with every session consisting of 5 hours Instructor led. The Timings will be usually from 10 AM to 3 PM.

## Data Science course Online Training available on Weekdays

Online Training is a 2 months programme on week days (Monday to Friday) with every session consisting of 1:30 hours Instructor led. The Timings will be usually from 7:30 AM to 9 AM.

# Data Science Course Highlights

#### Realtime Expert Trainers

Dedicated portal for Practice

Job Support

Weekly Assignments

Live Projects to work

# Who to Join Data Science course

#### IT Professionals,

Data Analysts, Business Analysts,

Functional Managers,

Graduates, Post Graduates

Also, anyone having interest in Data Science is free to attend our workshop.

# Data Science Job Roles Available

#### Business Intelligence Analyst

Data Mining Engineer

Data Architect

Data Scientist

Senior Data Scientist

# Data Science Job Market in 2018

#### Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university

5,000+ unique job postings for data scientists each month.

3,00,000+ Skilled Data Scientists required by the End of 2018.

Salaries range from $60,000 and up.

GlassDoor ranked it top place for popular jobs

# Data Science course Curriculum

**PART 1 : INTRODUCTION TO DATA SCIENCE:**

- What is Data Science? – Introduction.
- Importance of Data Science.
- Demand for Data Science Professional.
- Brief Introduction to Big data and Data Analytics.
- Lifecycle of data science.
- Tools and Technologies used in data Science.
- Comprehensive R Archive Network
- Demo of Installing R On windows from CRAN Website
- Installing R Studios on Windows OS Setting Up R Workspace.
- Getting Help for R-How to use help system
- Installing Packages – Loading And Unloading Packages

**PART 2 – STATISTICS**

**Fundamentals of Math and Probability Basic**- understanding of linear algebra, Matrics, vectors
- Addition and Multimplication of matrics Fundamentals of Probability
- Probability distributed function and cumulative distributed function.
- Class Hand-on
- Problem solving using R for vector manupulation
- Problem solving for probability assignments

**2 Descriptive Statistics**

- Describe or sumarise a set of data Measure of central tendency and measure of dispersion.
- The mean,median,mode, curtosis and skewness
- Computing Standard deviation and Variance.
- Types of distribution.

**Class Handson:**

- 5 Point summary BoxPlot
- Histogram and Bar Chart
- Exploratory analytics R Methods

**Inferential Statistics**- What is inferential statistics
- Different types of Sampling techniques
- Central Limit Theorem
- Point estimate and Interval estimate
- Creating confidence interval for population parameter
- Characteristics of Z-distribution and T-Distribution
- Basics of Hypothesis Testing
- Type of test and rejection region
- Type of errors in Hypothesis resting, Type-l error and Type-ll errors
- P-Value and Z-Score Method
- T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA) Regression analysis in ANOVA

**Class Hands-on:**

- Problem solving for C.L.T
- Problem solving Hypothesis Testing
- Problem solving for T-test, Z-score test
- Case study and model run for ANOVA, ANCOVA

**Hypothesis Testing**- Hypothesis Testing
- Basics of Hypothesis Testing
- Type of test and Rejection Region
- Type o errors-Type 1 Errors,Type 2 Errors
- P value method,Z score Method

**PART 3 – UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING**

**Introduction To Machine Learning**- What is Machine Learning?
- What is the Challenge?
- Introduction to Supervised Learning,Unsupervised Learning
- What is Reinforcement Learning?

**Linear Regression**- Introduction to Linear Regression
- Linear Regression with Multiple Variables
- Disadvantage of Linear Models
- Interpretation of Model Outputs
- Understanding Covariance and Colinearity
- Understanding Heteroscedasticity

**Case Study **

- Application of Linear
- Regression for Housing Price Prediction

**Logistic Regression**- Introduction to Logistic Regression.– Why Logistic Regression .
- Introduce the notion of classification Cost function for logistic regression
- Application of logistic regression to multi-class classification.
- Confusion Matrix, Odd’s Ratio And ROC Curve
- Advantages And Disadvantages of Logistic Regression.

**Case Study:**

- To classify an email as spam or not spam using logistic Regression.

**Decision Trees And Supervised Learning**- Decision Tree – data set
- How to build decision tree?
- Understanding Kart Model
- Classification Rules- Overfitting Problem
- Stopping Criteria And Pruning
- How to Find final size of Trees?
- Model A decision Tree.
- Naive Bayes
- Random Forests and Support Vector Machines
- Interpretation of Model Outputs

**Case Study:**

- Business Case Study for Kart Model
- Business Case Study for Random Forest
- Business Case Study for SVM

**Unsupervised Learning**- Hierarchical Clustering
- k-Means algorithm for clustering – groupings
- of unlabeled data points.
- Principal Component Analysis(PCA)- Data
- Independent components analysis(ICA)
- Anomaly Detection
- Recommender System-collaborative filtering algorithm

**Case Study**– Recommendation Engine for

e-commerce/retail chain

**Introduction to Deep Learning**- INeural Network
- Understaing Neural Network Model
- Understanding Tuning of Neural Network

**Case Study**: Case study using Neural Network

**Natural language Processing**- Introduction to natural Language
- Processing(NLP).
- Word Frequency Algorithms for NLP Sentiment Analysis

**Case Study :** Twitter data analysis using NLP

**Apache Spark Analytics**- What is Spark
- Introduction to Spark RDD
- Introduction to Spark SQL and Dataframes
- Using R-Spark for machine learning
- Hands-on:
- installation and configuration of Spark
- Hands on Spark RDD programming
- Hands on of Spark SQL and Dataframe programming
- Using R-Spark for machine learning programming

**PART 4 – R PROGRAMMING BASICS**

**R Basics, background**- Comprehensive R Archive Network
- Demo of Installing R On windows from CRAN Website
- Installing R Studios on Windows OS
- Setting Up R Workspace.
- Getting Help for R-How to use help system
- Installing Packages – Loading And Unloading Packages

**Getting familiar with basics**- Operators in R – Arithmetic,Relational,Logical and Assignment Operators
- Variables,Types Of Variables,Using variables Conditional statements,ifelse(),switch
- Loops: For Loops,While Loops,Using Break statement,Switch

**The R Programming Language- Data Types**- creating data objects from the keyword.
- How to make different type of data objects.
- Types of data structures in R
- Arrays And Lists- Create Access the elements
- Vectors – Create Vectors,Vectorized Operations,Power of Vectorized Operations Matrices- Building the first matrices,Matrix Operations,Subsetting,visualising subset
- Data Frames- create and filter data frames,Building And Merging data frames.

**Functions And Importing data into R**- Function Overview – Naming Guidelines
- Arguments Matching,Function with Multiple Arguments
- Additional Arguments using Ellipsis,Lazy Evaluation Multiple Return Values Function as Objects,Anonymous Functions
- Importing and exporting Data into R- importing from files like excel,csv and minitab.
- Import from URL and excel Files
- Import from database.

**Data Descriptive**- Statistics,Tabulation,Distribution
- Summary Statistics for Matrix Objects. apply() Command. Converting an Object into a Table
- Histograms, Stem and Leaf Plot, Density
- Normal Distribution

**Graphics in R – Types of graphics**- Bar Chart,Pie Chart,Histograms- Create and edit.
- Box Plots- Basics of Boxplots- Create and Edit Visualisation in R using ggplot2.
- More About Graphs: Adding Legends to Graphs,Adding Text to Graphs, Orienting the Axis Label.

**PART 5 – PYTHON FOR DATA SCIENCE**

**Python Programming Basics**- Installing Jupyter Notebooks
- Python Overview
- Python 2.7 vs Python 3
- Python Identifiers
- Various Operators and Operators Precedence
- Getting input from User,Comments,Multi line Comments.

**Making Decisions And Loop Control**- Simple if Statement,if-else Statement if-elif Statement.
- Introduction To while Loops.
- Introduction To for Loops,Using continue and break.

**Python Data Types: List,Tuples,Dictionaries**- Python Lists,Tuples,Dictionaries
- Accessing Values
- Basic Operations
- Indexing, Slicing, and Matrixes
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary

**Functions And Modules**- Introduction To Functions
- Why Defining Functions
- Calling Functions
- Functions With Multiple Arguments.
- Anonymous Functions – Lambda Using Built-In Modules,User-Defined Modules,Module Namespaces,Iterators And Generators.

**File I/O And Exceptional Handling**- Opening and Closing Files
- open Function,file Object Attributes
- close() Method ,Read,write,seek.Exception Handling,the try-finally Clause
- Raising an Exceptions,User-Defined Exceptions
- Regular Expression- Search and Replace
- Regular Expression Modifiers
- Regular Expression Patterns,re module.

**Numpy**- Introduction to Numpy. Array Creation,Printing Arrays
- Basic Operations- Indexing, Slicing and Iterating Shape Manipulation – Changing shape,stacking and spliting of array Vector stacking

**Pandas And Matplotlib**- Introduction to Pandas
- Importing data into Python
- Pandas Data Frames,Indexing Data Frames ,Basic Operations With Data frame,Renaming Columns,Subletting and filtering a data frame.
- Matplotlib – Introduction,plot(),Controlling
- Line Properties,Working with Multiple Figures,Histograms

**Introduction to Tableau/Spotfire**- Connecting to data source
- Creating dashboard pages
- How to create calculated columns
- Different charts
- Hands-on:
- Hands on on connecting data source and data clensing
- Hands on various charts
- Hands on deployment of Predictive model in visualisation

### Course Highlights

1. A Dedicated Portal For Practicing.

2. Real Time Project Data Models to Work

3. 1-1 Mentorship

4. Internship Offers for Freshers.

5. Weekly Assignments.

6. Weekly Doubt Sessions

7. Advanced Curriculum

8. Certificates On successful Completion of Project .

9. Resume Preparation Tips

10. Interview Guidance And Support.

11. Dedicated HR Team for Job Support And Placement Assistance.

12. Experienced Trainers.