Software Training Institute in Chennai with 100% Placements – SLA Institute

Easy way to IT Job

Data Science Online Course

(1987)
Live Online & Classroom Training
EMI
0% Interest

Our Data Science Online Training in Chennai will make students learn some of the most in-demand concepts in Data Science such as – Data types and data Models, Basic Operations in R Programming, Statistics, Probability, Data Visualization Techniques, Machine Learning etc. This curriculum will surely make students experts in the concept of  Data Science in a shorter span of time. Our Data Science Online Course with 100% placement support is curated with the help of leading experts from the IT industry, which makes our Data Science Online Course up-to-date in accordance with the latest trends.

Our SLA Institute is guaranteed to place you in high-paying Data Analyst and other Data Science related jobs with help of our experienced placement officers. SLA Institute’s Course Syllabus for Data Science covers all topics that are guaranteed to give you a complete understanding of the Data Science Online Course in Chennai.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
November 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
November 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

Save up to 20% in your Course Fee on our Job Seeker Course Series

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Placement

100% Assistance

Learning

Job-Centered Approach

Timings

Convenient Hrs

Mode

Online & Classroom

Certification

Industry-Accredited

This Course Includes

  • FREE Demo Class
  • 0% EMI Loan Facilities
  • FREE Softskill & Placement Training
  • Tie up with more than 500+ MNCs & Medium Level Companies
  • 100% FREE Placement Assistance
  • Course Completion Certificate
  • Training with Real Time Projects
  • Industry-Based Coaching By MNC IT Professionals
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Expected Criteria for Assured Placement

The following criteria help the placement team guide the candidates to get placed immediately after the course completion through SLA Institute.

  • 80% of coursework completion helps us arrange interviews in required companies.
  • 2 or 3 projects to be done for the selected course to ace the technical round effectively.
  • Ensure attending the placement training right from the first day of the selected course.
  • Practice well with resume building, soft skill, aptitude skill, and profile strengthening.
  • Utilize the internship training program at SLA for the complete technical skills.
  • Collect the course completion certificate and update the copy to the placement team.
  • Ensure your performance indicator meets the expectation of top companies.
  • Always be ready with the updated resume that includes project details done at SLA.
  • Enjoy unlimited interview arrangements along with internal mock interviews.
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+91 89256 88858

SLA's Distinctive Placement Approach

1

Tech Courses

2

Expert Mentors

3

Assignments & Projects

4

Grooming sessions

5

Mock Interviews

6

Placements

Objectives of Data Science Online Course in Chennai

The primary objective of our Data Science Online Course in Chennai is to make enrolled candidates experts in Data Science. This Data Science Online Course will make students grow into successful and most in-demand Data Analysts, and more. SLA Institute’s Data Science Online Course Curriculum is loaded with some of the most useful and rare concepts that will surely give students a complete understanding of Data Science. So, some of those concepts are discussed below:

  • To make students fully well-versed in fundamental Data Science like – Introduction to Business Analytics, Understanding Business Applications, Data types and data Models, Type of Business Analytics, Types of Objects in R etc.
  • To make students know more about Data Science by learning topics like – Statistics – Types of Distributions, Anova, F-Test, Central Limit Theorem & applications, Types of variables, Multiple Linear Regression, Logistic Regression, Market Basket Analysis etc.
  • To make students experts in advanced topics like Data Science like – Bubble Chart, Sparklines, Waterfall chart, Box Plot, Clustering, Direct Clustering Method, Mixture densities etc.

Scopes in the future for Data Science Online Course in Chennai

The following are the scopes available in the future for the Data Science Online Course:

  • AI and Machine Learning Integration: As AI and ML technologies advance, data scientists will increasingly focus on crafting sophisticated algorithms and models. The integration of AI into daily applications will heighten the demand for experts who can develop and fine-tune these systems.
  • Big Data Analytics: With the escalating volume and complexity of data, there will be a growing need for advanced techniques to handle and analyze big data. Data scientists will be crucial in devising methods for effective data storage, management, and insight extraction.
  • Data Privacy and Security: Increasing concerns over data privacy and security will drive a focus on protecting sensitive information. Data scientists will develop techniques to ensure data integrity, implement privacy-preserving measures, and adhere to regulations such as GDPR.
  • Ethical AI and Bias Mitigation: With AI systems playing a larger role in decision-making, there will be a stronger emphasis on ethical considerations and reducing biases in algorithms. Data scientists will need to create fair and transparent models.

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Data Science Course Syllabus

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SLA Institute’s Data Science Online Course Syllabus comes with 100% placement support so students will be guaranteed a placement in an esteemed organization. In addition to that, the Data Science Online Course Syllabus is also carefully curated with the help of leading professionals and experts from the IT industry with so many hours invested in it. So, everything that our students learn in the Data Science Online course is fully up-to-date to the current trends in the IT industry, which increases their chances of getting employed.

Introduction
  • 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

Project Practices on Data Science Training

Project 1Predictive Modeling for Sales Forecasting

Develop a predictive model to forecast future sales based on historical data. Use time series analysis and machine learning techniques to account for factors such as seasonality, promotions, and market trends.

Project 2Customer Segmentation Analysis

Use clustering algorithms to segment customers based on their purchasing behavior, demographics, or engagement levels. Create profiles for each segment to tailor marketing strategies and improve customer targeting.

Project 3Churn Prediction Model

Build a model to predict customer churn by analyzing historical customer data. Identify key factors leading to churn and develop strategies to retain at-risk customers.  

Project 4Sentiment Analysis on Social Media Data

Collect and analyze social media data to gauge public sentiment about a brand, product, or event. Use natural language processing (NLP) and sentiment analysis techniques to interpret user opinions and trends.

Prerequisites for learning Data Science Online Course in Chennai

SLA Institute does not demand any prerequisites for any course at all. SLA Institute has courses that cover everything from the fundamentals to advanced topics so whether the candidate is a beginner or an expert they will all be accommodated and taught equally in SLA Institute. However having a fundamental understanding of these concepts below will help you understand Data Science better, However it is completely optional:

  • Mathematics and Statistics: A strong grasp of mathematics, particularly calculus and linear algebra, alongside statistical concepts such as probability, hypothesis testing, and regression analysis, is essential. These mathematical principles are fundamental to many data science techniques and algorithms.
  • Programming Skills: Proficiency in at least one programming language commonly used in data science, such as Python or R, is critical. Familiarity with programming fundamentals, data manipulation, and coding practices will aid in developing data science solutions.
  • Data Manipulation and Analysis: Experience with data manipulation and analysis is important, including using libraries and tools for data cleaning and transformation (e.g., pandas for Python or dplyr for R). This knowledge is crucial for preparing data for analysis.
  • Understanding of Databases: Knowledge of databases and SQL (Structured Query Language) is important for retrieving and managing data. Being able to interact with relational databases and execute queries is a fundamental skill for data extraction.

Our Data Science Online Course in Chennai is apt for:

  • Students eager to excel in Data Science
  • Professionals considering transitioning to Data Science careers
  • IT professionals wanting to enhance their Data Science skills
  • Data Analysts who are looking forward to expanding their expertise.
  • Individuals searching opportunities in the Data Science field.

Job Profile for Data Science Online Course in Chennai

After finishing the Data Science Online Course in Chennai, students will be placed in various organizations through SLA Institute. This section will explore the various range of job profiles in which students can possibly be possible be placed as in the Data Science sector; 

  • Data Analyst: Data Science Online Course will train students into successful Data Analysts who will analyze complex datasets, generate reports, and offer insights to assist organizations in decision-making.
  • Data Scientist: Data Science Online Course will turn students into skilled Data Scientists who develop and apply predictive models, conduct advanced statistical analysis, and extract actionable insights from data.
  • Business Intelligence (BI) Analyst: Data Science Online Course will make students into Business Intelligence (BI) Analyst who will create and implement BI solutions, analyze business data to support decisions, and develop dashboards and reports.
  • Data Engineer: The SLA Institute will provide students with enough resources that it will train students into successful Data Engineer who will be doing database management, ETL processes, programming (Python, Java), and big data technologies (e.g., Hadoop, Spark).
  • Quantitative Analyst: The SLA Institute will turn students into skilled Quantitative Analysts who will utilize mathematical and statistical models to analyze financial data, guiding investment strategies and risk management.
  • Data Architect: The SLA Institute will make students into Data Architect who will design and oversee data systems and architectures to support integration and governance.
  • Machine Learning Engineer: Create and deploy machine learning models to address complex issues and enhance processes.
  • Marketing Analyst: Examine marketing data to assess campaign effectiveness, understand customer behavior, and track market trends.
  • Operations Analyst: Analyze operational data to refine business processes, improve performance, and cut costs.
  • Healthcare Data Analyst: Analyze healthcare data to enhance patient outcomes, optimize treatments, and support healthcare decisions.
  • Product Analyst: Assess product performance, analyze user behavior, and provide insights to drive product strategy and development.
  • Financial Analyst: Examine financial data to aid in investment decisions, budget planning, and financial forecasting.

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The Placement Process at SLA Institute

  • To Foster the employability skills among the students
  • Making the students future-ready
  • Career counseling as and when needed
  • Provide equal chances to all students
  • Providing placement help even after completing the course

Data Science Course FAQ

What distinguishes supervised learning from unsupervised learning, and when should each be applied?

Supervised learning uses labeled data to train models for tasks like classification (e.g., spam detection) and regression (e.g., forecasting prices). Unsupervised learning works with unlabeled data to find patterns or groupings, such as clustering (e.g., market segmentation) and dimensionality reduction (e.g., PCA). Use supervised learning for prediction tasks with known outcomes and unsupervised learning for exploring data structure or identifying groupings.

How can class imbalance be addressed in machine learning models?

Class imbalance can be managed using:

  • Resampling: Adjust the dataset by oversampling the minority class or undersampling the majority class.
  • Synthetic Data Generation: Apply techniques like SMOTE to create synthetic data for underrepresented classes.
  • Class Weights: Modify model training to give higher weight to the minority class.
  • Ensemble Methods: Use techniques such as balanced random forests or boosting to handle imbalance.
What methods are effective for feature selection in machine learning?

Key methods include:

  • Filter Methods: Use statistical tests (e.g., chi-square) to assess feature relevance.
  • Wrapper Methods: Apply techniques like Recursive Feature Elimination (RFE) to evaluate feature subsets based on model performance.
  • Embedded Methods: Use algorithms that perform feature selection within the model, such as LASSO or tree-based methods.
How do I select the right evaluation metric for a machine learning model?

Choose metrics based on the problem:

  • Accuracy: Suitable for balanced classification problems.
  • Precision, Recall, and F1 Score: Ideal for imbalanced classification to evaluate the trade-off between true positives and false positives.
  • Mean Absolute Error (MAE) and Mean Squared Error (MSE): Used in regression to measure prediction errors, with MAE being robust to outliers.
  • ROC-AUC: Useful for binary classifiers to assess performance across various thresholds.
Where is the corporate office of the SLA Institute located?

The corporate office of the SLA Institute is located at the K.K.Nagar branch.

EMI available in the SLA Institute?

Yes, the SLA Institute does indeed provide EMI to students in payments with 0% interest. 

Is it hard to learn the Data Science Course in Online mode?

Learning the Data Science Course in Online mode can be easy if students are dedicated to the course by cooperating with trainers and completing the projects within the deadline.

How long is the Data Science Online Course?

The Data Science Online Course is 6 months long.

On Average Students Rated The Data Science Course 4.90/5.0
(1987)
See More Reviews

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