Preaload Image

About Data Science

A Data Scientist is someone who is better at Statistics than any software Engineer and better at Software Engineering than any Statistician. They are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role in data analysis is becoming increasingly important as businesses rely more heavily on big data and data analytics to drive decision-making.

Socialprachar.com offers Best Data Science Classroom Training in Hyderabad with Real time experienced trainers. Be a Certified Data Scientist in 90 Days by mastering R, Python, Machine Learning, Deep learning, Statistics, Tableau, Hadoop and SQL.

✓ Mentorship

✓ Certifications

✓ Practical Sessions

✓ Weekly Assignments

✓ Mock Interviews

✓ Experienced Trainers

Advanced Curriculum

✓ Interview Guidance

Dedicated Portal

Get ₹3000 Off this festive Season! Enroll here

Select Class Type

Who Can 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.

Who Can be a Data Scientist?

A Data Scientist is sort of ‘jack-of-all-trades’ for data crunching. Basically, 3 main skills a Data scientist needs to possess are mathematics/statistics, computer programming literacy and knowledge of particular business.

What is the Payscale of Data Scientist?

A data scientist earn an average salary of Rs 6,00,000 per year.
Experience influences the income of this job. The salary for a data scientist abroad can range anywhere from $100,000 to $120,000.

What are the requisite skill set to be a Data Scientist?

Expertise in mathematics, technical and programming skills, business and strategy awareness combine to form Data Science.

Quick list

Data Science Course Curriculum

PART 1 : INTRODUCTION TO DATA SCIENCE:

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

PART 2 –  STATISTICS

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

 

2 Descriptive Statistics

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

Class Handson:

  1. 5 Point summary BoxPlot
  2. Histogram and Bar Chart
  3. Exploratory analytics R Methods

 

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

 

Class Hands-on:

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

 

  1. Hypothesis Testing
  2. Hypothesis Testing
  3. Basics of Hypothesis Testing
  4. Type of test and Rejection Region
  5. Type o errors-Type 1 Errors,Type 2 Errors
  6. P value method,Z score Method

PART 3 – UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

  1. Introduction To Machine Learning
  2. What is Machine Learning?
  3. What is the Challenge?
  4. Introduction to Supervised Learning,Unsupervised Learning
  5. What is Reinforcement Learning?

 

  1. Linear Regression
  2. Introduction to Linear Regression
  3. Linear Regression with Multiple Variables
  4. Disadvantage of Linear Models
  5. Interpretation of Model Outputs
  6. Understanding Covariance and Colinearity
  7. Understanding Heteroscedasticity

 

Case Study

  • Application of Linear
  • Regression for Housing Price Prediction

 

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

 

Case Study:

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

 

  1. Decision Trees And Supervised Learning
  2. Decision Tree – data set
  3. How to build decision tree?
  4. Understanding Kart Model
  5. Classification Rules- Overfitting Problem
  6. Stopping Criteria And Pruning
  7. How to Find final size of Trees?
  8. Model A decision Tree.
  9. Naive Bayes
  10. Random Forests and Support Vector Machines
  11. Interpretation of Model Outputs

Case Study:

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

 

  1. Unsupervised Learning
  2. Hierarchical Clustering
  3. k-Means algorithm for clustering – groupings
  4. of unlabeled data points.
  5. Principal Component Analysis(PCA)- Data
  6. Independent components analysis(ICA)
  7. Anomaly Detection
  8. Recommender System-collaborative filtering algorithm

Case Study– Recommendation Engine for

e-commerce/retail chain

 

  1. Introduction to Deep Learning
  2. INeural Network
  3. Understaing Neural Network Model
  4. Understanding Tuning of Neural Network

 

Case Study: Case study using Neural Network

 

  1. Natural language Processing
  2. Introduction to natural Language
  3. Processing(NLP).
  4. Word Frequency Algorithms for NLP Sentiment Analysis

 

Case Study : Twitter data analysis using NLP

 

  1. Apache Spark Analytics
  2. What is Spark
  3. Introduction to Spark RDD
  4. Introduction to Spark SQL and Dataframes
  5. Using R-Spark for machine learning
  6. Hands-on:
  7. installation and configuration of Spark
  8. Hands on Spark RDD programming
  9. Hands on of Spark SQL and Dataframe programming
  10. Using R-Spark for machine learning programming

PART  4 – R PROGRAMMING

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

 

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

 

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

 

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

 

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

 

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

PART 5 – PYTHON FOR DATA SCIENCE

  1. Python Programming Basics
  2. Installing Jupyter Notebooks
  3. Python Overview
  4. Python 2.7 vs Python 3
  5. Python Identifiers
  6. Various Operators and Operators Precedence
  7. Getting input from User,Comments,Multi line Comments.

 

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

 

  1. Python Data Types: List,Tuples,Dictionaries
  2. Python Lists,Tuples,Dictionaries
  3. Accessing Values
  4. Basic Operations
  5. Indexing, Slicing, and Matrixes
  6. Built-in Functions & Methods
  7. Exercises on List,Tuples And Dictionary

 

  1. Functions And Modules
  2. Introduction To Functions
  3. Why Defining Functions
  4. Calling Functions
  5. Functions With Multiple Arguments.
  6. Anonymous Functions – Lambda Using Built-In Modules,User-Defined Modules,Module Namespaces,Iterators And Generators.

 

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

 

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

 

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

PART 6 – DEEP LEARNING

PART 7 – NLP

PART 8 – TABLEAU/SPOTFIRE

PART 9 – HADOOP

  1. Connecting to data source
  2. Creating dashboard pages
  3. How to create calculated columns
  4. Different charts
  5. Hands-on:
  6. Hands on on connecting data source and data clensing
  7. Hands on various charts
  8. Hands on deployment of Predictive model in visualisation

FINAL PROJECT(S)

MENTORSHIP

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.

Feel Free to contact us at +91 8019 479 419

Frequently Asked Questions

What about Your Data Science Course Details ?

We are Providing 3 Month and 6 Month Curriculums on Data Science includes 2 Real Time Projects, MNC certifications, Job support, Community Meetups, Bootcamps etc to Build your Career into data science field.

What type of Training Modes You Provide?

Right Now we are offering Daily, Weekend, Online modes of Training. Check our Upcoming Batches page for the latest coming up Classroom,Online and weekend batch schedules.

Who are Eligible to Learn Data Science?

  • IT Professionals,
  • Data Analysts,
  • Business Analysts,
  • Functional Managers,
  • Graduates,
  • Post Graduates
  • Also, anyone having interest in Data Science

What skills do I need to Master Data Science?

You need to get skilled on following, in order to take a deep dive in this field.

  • Statistic and probability
  • Algorithms
  • Programming Languages (Java, Scala ,SQL, R, Python)
  • Data mining
  • Machine learning
  • Deep Learning

What is the Data Science Job Market Now ?

  • Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
  • 6,000 unique job postings for data scientists each week.
  • 3,00,000+ Skilled Data Scientists required by the End of 2018.
  • salaries range from Min 3L per Annum to 20 L per Annum.
  • GlassDoor ranked it top place for popular jobs

Do You Provide Job Placements ?

Yes, We have a Dedicated HR team for Placement support Like Resume Preparations, Mock Interviews, Main Interviews, Company Tie-ups etc

What Type of Certifications You Provide After the course ?

We Provide Data Science Mastery Certificate from Social Prachar and Two More MNC certifications. Get In touch with the Team for more details about all our certifications.

Do You Provide Hands-on Experience ?

Yes All the data science Trainees need to work on min 2 Real Time projects for Better understanding of Real Time Data Science Methods and Techniques.

What about Trainer Faculty experience ?

Our Trainer Faculty is from Top MNC’s who has 10+ years of Experience with Real time clients. They will Mentor you all the way till you will get confidence on the subject. We have Community meetups,Boot Camps,workshops etc where you can meet the industry experts.

What is data Science ?

Data Science is the New Buzz word is the Market Right Now !

Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. A Data Scientist is someone who is better at Statistics than any software Engineer and better at Software Engineering than any Statistician. They are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals.

I am 2018 Fresher Am i Eligible to Learn Data Science course?

Yes, You are Eligible to Learn Data Science Course If you are Okay to Learn Programming and Good with Mathematics and Business Knowledge

What are the Modules you cover in Data Science Course ?

We will Start from basics Like Introduction to Data science,R,Python,Deep Learning,Statistics,Machine Learning, Hadoop, Spark, SQL Etc with Two Real Time Projects at the End for Better Understanding of Data Science.

I have 3+ Years IT experience, am i Eligible to Learn Data Science Course?

Yes, Undoubtedly Your Profile is Eligible to Learn Data Science course.

I'm from EEE background , can I go for data science , I don't have basics of any programming language

Yes, you can if you are willing to learn python programming and good with mathematics and statistics.

I don't have any engineering background can I still learn data Science.

All Bsc, BCom, BTech, MBA, MTech MCA are eligible to learn Data Science.

Will this be best option for MBA finance students? How will it be helpful for finance students??

Now its booming so anyone who completed Btech/MBA/Degree etc can learn, but only if you are willing to learn programming.

I'm weak at Mathematics, Can I opt Data Scientist as my career ?

Yes you can opt but need to work a lot on maths. Our team will provide you the required material to overcome.

I'm from Pharmacy background. Please suggest me whether am I applicable to do this course?

Yes, IF, You have less than 0-2 years in Pharmacy you can learn. And also if u are willing to learn programming you can learn data science.

I'm from mechanical background, but i want to learn data science , is it possible to understand without knowledge in languages like c , java knowledge

Yes, you can learn easily. Our curriculum starts from scratch, so you can learn without much confusions.

Hello am from medical science field am I eligible for this course

No. If its just for learning no problem. But if u need to make a career into data science, it will be little tough.

Is there any minimum percentage criteria of graduates for jobs in data science

No Minimum criteria but need to have good knowledge on Mathematics.

Upcoming Batches
 
Make this festive season a Great start for your Career. Get 3000 OFF, Only for You ! Limited offer.

Applicable on all courses. Please fill our short form to avail the offer.

You would like to go for

Your preferred modeClassroom TrainingOnline Training

X
Get ₹ 3000 off