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

Easy way to IT Job

Data Science Course in OMR

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

Start your journey to data mastery with our Data Science Training in OMR at SLA Institute! Our Data Science Course in OMR with 100% Placement Support is designed to equip both novices and experienced professionals with cutting-edge skills and practical knowledge. Dive into key areas such as data analysis, machine learning, and data visualization through engaging, hands-on sessions led by industry experts. At SLA Institute, we offer state-of-the-art facilities, real-world project experience, and personalized mentorship to help you build a powerful portfolio and advance your career. With our comprehensive support and career guidance, you’ll be well-prepared for success in the data science field. Join us in OMR and transform your future with our expert-led training!

At SLA Institute, we guarantee placement in a high-paying Developer job with the support of our experienced placement officers. Our Data Science Course Syllabus covers all essential topics, providing you with a comprehensive understanding of Data Science development.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
October 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
October 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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Quick Enquiry

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.
Have Queries? Ask our Experts

+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 Course in OMR

The Data Science Course in OMR aims to achieve several goals to help you become proficient in web development using Data Science. Throughout the course, you’ll:

  • Learn Data Analysis: Understand how to analyze and interpret data to extract meaningful insights.
  • Master Machine Learning: Get to know machine learning techniques to build and apply predictive models.
  • Create Data Visualizations: Learn to make clear and effective visualizations that present data findings.
  • Gain Practical Experience: Work on hands-on projects to apply what you’ve learned and build a strong portfolio.
  • Use Key Tools: Familiarize yourself with essential tools and technologies like Python, R, SQL, and data science libraries.

Advance Your Career: Benefit from career support, including portfolio building and job placement assistance.

Future Scope of Data Science Course in OMR

Growing Demand for Data Experts

The future of a Data Science Course in OMR looks bright because more and more companies need skilled data professionals. As businesses collect more data, they need experts to analyze it and help make smart decisions. Data scientists are in high demand across many industries, such as finance, healthcare, retail, and technology, where they help improve operations and drive innovation.

Many Career Opportunities

Completing a Data Science Course in OMR opens up a range of career options. With skills in data analysis, machine learning, and data visualization, you can work as a Data Analyst, Data Scientist, Machine Learning Engineer, or Business Intelligence Analyst. These skills also allow you to advance in various fields, including research, consulting, and management roles.

Technological Advancements

Data science is at the cutting edge of technology. New developments in artificial intelligence (AI), big data, and predictive analytics are constantly emerging. As these technologies advance, data scientists will have new tools and techniques to explore, making the field exciting and full of possibilities.

Ongoing Learning

Data science is a fast-changing field. While a Data Science Course in OMR provides a solid base, staying current with new tools and trends is important. Continuing education and advanced certifications will help you stay relevant and successful as the field evolves.

Entrepreneurial Possibilities

The rise of data also means more opportunities for starting your own business. Graduates from a Data Science Course in OMR can create their own data-focused companies or consultancies. By using their skills, they can offer valuable insights and help other businesses succeed.

Achieve Your Goals With SLA

SLA builds your future with comprehensive coursework and unparalleled placement support.
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Data Science Course Syllabus

Download Syllabus

Join our Data Science Training in OMR at SLA Institute to excel in the field of data science. This comprehensive program covers everything from basic programming skills to advanced data science techniques. Gain expertise in managing databases, deploying applications, and working with powerful frameworks. Benefit from personalized job placement support and hands-on projects that prepare you for a successful career. Get expert guidance and start your journey in data science with us. Enroll today to explore the exciting opportunities in data science.

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 1Recommendation System

Create a tool that suggests products, movies, or content based on what users like or have previously chosen. This project will teach you how to build systems that offer personalized recommendations.

Project 2Fraud Detection

Develop a model to spot fraudulent transactions in financial data. Use machine learning techniques to find unusual patterns that might indicate fraud. This project will show you how to identify and prevent fraud in financial transactions.

Project 3Healthcare Predictions

Analyze healthcare data to forecast patient outcomes, like disease progression or the likelihood of readmission. This project will help you learn how to use data to improve patient care and make better healthcare decisions.

Project 4Customer Churn Prediction

Build a model to predict which customers might cancel their subscriptions. This will help businesses take steps to keep these customers. This project will teach you how to use data to understand and reduce customer churn.

Prerequisites for learning Data Science Course in OMR

To join our Data Science Training in OMR at SLA Institute, no specific prior knowledge is required. Whether you’re new to programming or have some experience, everyone is welcome. However, having a foundational understanding of the following can be beneficial:

  • Math and Stats: Know basic math and statistics to understand and analyze data.
  • Programming: Learn programming languages like Python or R to work with data.
  • Data Handling: Be familiar with cleaning and managing data.
  • Databases: Understand the basics of databases and SQL for handling data.
  • Analytical Skills: Have good problem-solving skills to make sense of data.
  • Willingness to Learn: Be interested in data science and ready to dive into complex topics.

Our Data Science Course in OMR is ideal to:

  • Students eager to excel in Data Science 
  • Professionals considering transitioning to Data Science careers
  • IT professionals aspiring to enhance their Data Science skills
  • Data Analysts enthusiastic about expanding their expertise
  • Individuals seeking opportunities in Data Science

Job Profile in Data Science Course in OMR

In the Data Science Course in OMR at SLA Institute, participants are prepared for various job profiles that require expertise in both front-end and back-end data handling:

  • Data Scientist
    • Role: Examine and understand complex data to assist organizations in making informed, data-driven decisions.
    • Responsibilities: Develop statistical models, perform data mining, create data visualizations, and present findings to stakeholders.
    • Salary: ₹8,00,000 – ₹15,00,000 per year, depending on experience and location.
  • Data Analyst
    • Role: Collect, process, and analyze data to support business decisions and strategies.
    • Responsibilities: Generate reports, create dashboards, and identify trends and patterns in data.
    • Salary: ₹5,00,000 – ₹10,00,000 per year.
  • Machine Learning Engineer
    • Role: Design and implement machine learning models and algorithms for predictive and classification tasks.
    • Responsibilities: Build and train models, optimize algorithms, and deploy machine learning solutions.
    • Salary: ₹9,00,000 – ₹18,00,000 per year.
  • Business Intelligence (BI) Analyst
    • Role: Use data to help organizations understand their business performance and make strategic decisions.
    • Responsibilities: Create reports, design dashboards, analyze business trends, and support data-driven decision-making.
    • Salary: ₹6,00,000 – ₹12,00,000 per year.
  • Data Engineer
    • Role: Build and maintain the infrastructure needed for data generation, storage, and analysis.
    • Responsibilities: Develop data pipelines, manage data warehouses, and ensure data quality and availability.
    • Salary: ₹7,00,000 – ₹14,00,000 per year.
  • Quantitative Analyst (Quant)
    • Role: Apply mathematical and statistical methods to financial and risk management problems.
    • Responsibilities: Develop and implement quantitative models, analyze financial data, and assess risk.
    • Salary: ₹10,00,000 – ₹20,00,000 per year.

These roles reflect the diverse career opportunities available for professionals with data science training, each offering unique responsibilities and competitive salaries.

Want to learn with a personalized course curriculum?

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

Is Data Science easy or hard?

Data Science can be tough because it involves programming, math, and analysis. But with practice and effort, it gets easier. The key is to keep learning and applying what you know to real-world situations.

What is Data Science used for?

Data Science is used to analyze data and help businesses make better decisions. It’s applied in areas like healthcare, finance, and marketing to predict outcomes, detect fraud, and create recommendations, turning data into valuable insights and solutions.

Is Data Science enough to get a job?

Yes, Data Science skills can help you get a job, especially in areas like finance, healthcare, and tech. But having good programming, analytical skills, and practical experience with real projects will make your chances even better.

Does SLA Institute have HR personnel?

Yes, SLA Institute has an HR personnel who will look into students issues and grievances.

Does SLA Institute support EMI options?

Yes, SLA Institute supports EMI options with 0% interest.’

Is Data Science good career?

Yes, Data Science is a great career choice. It offers high demand, good salaries, and opportunities in various industries like finance, healthcare, and technology. With continuous learning and skill development, it can lead to a rewarding and successful career.

Does SLA Institute provide Lifetime Placement Support?

Yes, SLA Institute provides Lifetime Placement Support to assist students in securing job placements throughout their careers.

On Average Students Rated The Data Science Course 4.80/5.0
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