Data Science Training in Chennai

data-science-training-in-chennai

Enroll for the most demanding skill in the world now. Data Science Training in Chennai from Softlogic will take your career to a new height. We offer you an amazing platform to study and explore the subject from experts. Python, R, artificial intelligence, machine learning, SAS, Clinical SAS are the data science courses offered by us.

Best Data Science Training Institute in Chennai

What is Data Science?

Data Science deals with mining hidden insights of data related to trends, behavior, understanding and inferences to enable informed decisions to support the business. The professionals who do these tasks are considered as Data Scientist. Data Science is the most demanded profession now.

Is there a huge demand for Data Science?

The answer is a simple yes. Business organizations are realizing the value of assessing data so as to take informed decisions and enhance their business. Digitalization in every aspects of business is assisting them to generate the data and initiate evaluation of data.Therefore several opportunities are created in this space. Since there is a great demand for data science engineers the salaries offered for them are also high. Data scientist career path is long and highly paid since the production of online data is constant.

Data Science Job Opportunities

Data science/analytics is forming plenty of jobs in all the domains across the world. The raise in demand for people who are proficient in mining and interpreting data is a standing proof for the great job opportunities in data science domain. Every company has something to do with data. This is the reason they are continuously looking for people with data science knowledge. Ranging from start-ups to big companies, most of the organizations have the requirement for experienced professionals. When you pursue a data science course at Softlogic, you will be able to work in positions including analytics manager, business analyst, data analyst, research analyst, statistician etc. In order to ensure that you get the high-paying job in the market, we provide placement support too.

Modes of Data Science Training

You needn’t worry if time and place stops you from taking Data Science Course in Chennai from Softlogic.

We offer both online and classroom-based Data Science Training in Chennai.

Data Science Course in Chennai

Our curriculum is exhaustive, unique and standard. Our expert trainers impart the data science concepts in such a way that every word makes sense to the candidates.

We provide data science training in the following areas:

  • Python– Learn this versatile language and stay ahead in the competition.
  • R Programming– Being both a statistical tool and object-oriented programming, R is the right choice to take your career to the next level.
  • Artificial Intelligence– Get to know the  most happening topic in the world right now. Learn artificial intelligence from experts.
  • Machine Learning– Ths is one of the technologies that is shaping this era. Gain better insight on this from proficient trainers.
  • SAS– Learn this oldest data analytical tool and become an expert
  • Clinical SAS– Learn this application of SAS technology in clinical domain.

You can opt for any of the above-mentioned courses and get trained from expert faculties.

Data Science Training Placement Assistance

If you are an experienced professional who is looking for a career in data science, then Softlogic assists in placement in organizations. “Training to job placement” is our key strength. We carry out the required assistance till you are placed.

Prerequisites to Learn Data Science Course

There are no big prerequisites for learning data science. However, knowledge in programming, linear algebra and statistics is an added advantage.

Who can attend Data Science Training

Our Data Science Course in Chennai concentrates mainly on the big data services. Hence, all aspirants of machine learning techniques should take up this course to enhance their development and build a career in the same field. Individuals who have exposure to Java and mathematical aptitude can gain if they get into data science training from Softlogic. Hadoop experts can also benefit from data science training.

  • SAS professionals and SPSS professionals who want to get good comprehension of big data analytics.
  • Software developers who want to become data scientists.
  • Business analysts who are inclined in learning machine techniques.
  • R professionals who want to dive deep into the big data analysis field
  • Experienced analytics managers who lead a team of analysis.
  • Professionals from any stream  who have sufficient logical, mathematical and analytical skills.
  • Hadoop experts who want to learn both machine learning execution and R.
  • Statisticians who want to execute the techniques of statistics of huge data on Big data.
  • Information architects who want to gain expertise in predictive analysis.
  • Analysts who want to obtain reasonable knowledge in the concepts of data science.

In the scheduled duration, our data science course gives great insight on the topic. Call us (+91 86818 84318) for a free demo session now.

Our Data Science course is divided into three levels, Associate, Developer and Professionals:

Data Science Associate – Includes R Programming + Statistics  + Machine Learning with R + Tableau for Data Visualization (Duration – 70 Hours)

Data Science Developer – Includes R Programming + Statistics  + Machine Learning with R + Tableau for Data Visualization + Python Programming (Duration – 120 Hours)

Data Science Professionals – Includes R Programming + Statistics  + Machine Learning with R + Tableau for Data Visualization +  Machine Learning with Python + Deep Learning with Python + Neural Network with Python + NLP with Python + Tensor Flow (Duration – 120 Hours)

Data Science Training Course Fee and Duration

The data science course fees is moderate and can be paid in installments. The aspiring candidate can contact our helpdesk regarding flexible timings.

Duration
Hours
Training Mode

Regular Track

45 – 60 Days

2 hours a day

Live Classroom

Weekend Track

8 Weekends

3 hours a day

Live Classroom

Fast Track

5 Days

6+ hours a day

Live Classroom

This is an approximate course fee and duration for Data Science. Please contact our team for current Data Science course fee and duration.

Data Science Training Course Syllabus

Data Science Associate (Detailed Syllabus)

Introduction to Data Science

  • Introduction to Data Analytics
  • Introduction to Business Analytics
  • Understanding Business Applications
  • Data types and data Models
  • Type of Business Analytics
  • Evolution of Analytics
  • Data Science Components
  • Data Scientist Skillset
  • Univariate Data Analysis
  • Introduction to Sampling

Basic Operations in R Programming

  • Introduction to R programming
  • Types of Objects in R
  • Naming standards in R
  • Creating Objects in R
  • Data Structure in R
  • Matrix, Data Frame, String, Vectors
  • Understanding Vectors & Data input in R
  • Lists, Data Elements
  • Creating Data Files using R

Data Handling in R Programming

  • Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
  • Sub-setting Data
  • Selecting (Keeping) Variables
  • Excluding (Dropping) Variables
  • Selecting Observations and Selection using Subset Function
  • Merging Data
  • Sorting Data
  • Adding Rows
  • Visualization using R
  • Data Type Conversion
  • Built-In Numeric Functions
  • Built-In Character Functions
  • User Built Functions
  • Control Structures
  • Loop Functions

Introduction to Statistics

  • Basic Statistics
  • Measure of central tendency
  • Types of Distributions
  • Anova
  • F-Test
  • Central Limit Theorem & applications
  • Types of variables
  • Relationships between variables
  • Central Tendency
  • Measures of Central Tendency
  • Kurtosis
  • Skewness
  • Arithmetic Mean / Average
  • Merits & Demerits of Arithmetic Mean
  • Mode, Merits & Demerits of Mode
  • Median, Merits & Demerits of Median
  • Range
  • Concept of Quantiles, Quartiles, percentile
  • Standard Deviation
  • Variance
  • Calculate Variance
  • Covariance
  • Correlation

Introduction to Statistics – 2

  • Hypothesis Testing
  • Multiple Linear Regression
  • Logistic Regression
  • Market Basket Analysis
  • Clustering (Hierarchical Clustering & K-means Clustering)
  • Classification (Decision Trees)
  • Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)

Introduction to Probability

  • Standard Normal Distribution
  • Normal Distribution
  • Geometric Distribution
  • Poisson Distribution
  • Binomial Distribution
  • Parameters vs. Statistics
  • Probability Mass Function
  • Random Variable
  • Conditional Probability and Independence
  • Unions and Intersections
  • Finding Probability of dataset
  • Probability Terminology
  • Probability Distributions

Data Visualization Techniques

  • Bubble Chart
  • Sparklines
  • Waterfall chart
  • Box Plot
  • Line Charts
  • Frequency Chart
  • Bimodal & Multimodal Histograms
  • Histograms
  • Scatter Plot
  • Pie Chart
  • Bar Graph
  • Line Graph

Introduction to Machine Learning

  • Overview & Terminologies
  • What is Machine Learning?
  • Why Learn?
  • When is Learning required?
  • Data Mining
  • Application Areas and Roles
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement learning

Machine Learning Concepts & Terminologies

Steps in developing a Machine Learning application

  • Key tasks of Machine Learning
  • Modelling Terminologies
  • Learning a Class from Examples
  • Probability and Inference
  • PAC (Probably Approximately Correct) Learning
  • Noise
  • Noise and Model Complexity
  • Triple Trade-Off
  • Association Rules
  • Association Measures

Regression Techniques

  • Concept of Regression
  • Best Fitting line
  • Simple Linear Regression
  • Building regression models using excel
  • Coefficient of determination (R- Squared)
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Variable transformation
  • Reading coefficients in MLR
  • Multicollinearity
  • VIF
  • Methods of building Linear regression model in R
  • Model validation techniques
  • Cooks Distance
  • Q-Q Plot
  • Durbin- Watson Test
  • Kolmogorov-Smirnof Test
  • Homoskedasticity of error terms
  • Logistic Regression
  • Applications of logistic regression
  • Concept of odds
  • Concept of Odds Ratio
  • Derivation of logistic regression equation
  • Interpretation of logistic regression output
  • Model building for logistic regression
  • Model validations
  • Confusion Matrix
  • Concept of ROC/AOC Curve
  • KS Test

Market Basket Analysis

  • Applications of Market Basket Analysis
  • What is association Rules
  • Overview of Apriori algorithm
  • Key terminologies in MBA
  • Support
  • Confidence
  • Lift
  • Model building for MBA
  • Transforming sales data to suit MBA
  • MBA Rule selection
  • Ensemble modelling applications using MBA

Time Series Analysis (Forecasting)

  • Model building using ARIMA, ARIMAX, SARIMAX
  • Data De-trending & data differencing
  • KPSS Test
  • Dickey Fuller Test
  • Concept of stationarity
  • Model building using exponential smoothing
  • Model building using simple moving average
  • Time series analysis techniques
  • Components of time series
  • Prerequisites for time series analysis
  • Concept of Time series data
  • Applications of Forecasting

Decision Trees using R

  • Understanding the Concept
  • Internal decision nodes
  • Terminal leaves.
  • Tree induction: Construction of the tree
  • Classification Trees
  • Entropy
  • Selecting Attribute
  • Information Gain
  • Partially learned tree
  • Overfitting
  • Causes for over fitting
  • Overfitting Prevention (Pruning) Methods
  • Reduced Error Pruning
  • Decision trees – Advantages & Drawbacks
  • Ensemble Models

K Means Clustering

  • Parametric Methods Recap
  • Clustering
  • Direct Clustering Method
  • Mixture densities
  • Classes v/s Clusters
  • Hierarchical Clustering
  • Dendogram interpretation
  • Non-Hierarchical Clustering
  • K-Means
  • Distance Metrics
  • K-Means Algorithm
  • K-Means Objective
  • Color Quantization
  • Vector Quantization

Tableau Analytics

  • Tableau Introduction
  • Data connection to Tableau
  • Calculated fields, hierarchy, parameters, sets, groups in Tableau
  • Various visualizations Techniques in Tableau
  • Map based visualization using Tableau
  • Reference Lines
  • Adding Totals, sub totals, Captions
  • Advanced Formatting Options
  • Using Combined Field
  • Show Filter & Use various filter options
  • Data Sorting
  • Create Combined Field
  • Table Calculations
  • Creating Tableau Dashboard
  • Action Filters
  • Creating Story using Tableau

Analytics using Tableau

  • Clustering using Tableau
  • Time series analysis using Tableau
  • Simple Linear Regression using Tableau

R integration in Tableau

  • Integrating R code with Tableau
  • Creating statistical model with dynamic inputs
  • Visualizing R output in Tableau
  • Case Study 1- Real time project with Twitter Data Analytics
  • Case Study 2- Real time project with Google Finance
  • Case Study 3- Real time project with IMDB Website