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

Data Science Training In OMR

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Join Our 100% Job-Guaranteed Data Science Course in OMR. We Provide Quality Data Science training with an affordable Cost in OMR. Our Data Science Syllabus Covers Python programming, statistics, data wrangling, data visualization, machine learning, deep learning, and big data analytics. At Softlogic Systems, you’ll earn globally recognized certifications, build real-time projects, and receive complete placement assistance to launch a successful career in your chosen field.

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Syllabus of Data Science Course In OMR

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

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

Objectives of Data Science Course In OMR

As it was mentioned before, the curriculum and syllabus of the data science course in OMR are crafted and curated with precise care and consideration for the betterment of students. The syllabus covers a wide range of topics in its chapters, ranging from the basic introduction to Python Data Science to advanced data visualization with SEABORN and more. Here are some of the skills that can be acquired at our Data Science Institute, OMR:

  • Candidates will learn about the introduction to how the integration of Python in data science can be a helpful tool in detail.
  • Candidates will learn about Pandas- features, data structures, and more.
  • Candidates will be exposed to numerical computation through Numpy
  • Candidates will also get a lot of hands-on practical knowledge on the Matplotlib library and data distributions using SEABORN, Numpy, and Pandas.
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Highlights of Data Science Course In OMR?

Data science is a field of methodologies that are used to study data and derive insights from that data for businesses. Data science employs techniques like machine learning algorithms, statistics, and computer engineering to analyze those data. 

  • Increase in Demand: With the ever-growing volume of data, industries are increasingly seeking data scientists to extract actionable insights from it.
  • Personal Development: Being well-versed in data science gives an individual problem-solving, critical thinking, and analytical skills that also help their well-being.
  • Add-On Skills Set: By learning Data Science, an individual can also become well-versed in associated programming languages like R, Python, etc. 
  • Versatility: Data science skills are applicable across diverse industries like healthcare, finance, technology, and marketing, fostering flexibility in career paths.
  • Proficiency in Programming: Mastery of a programming language like Python or R is crucial, encompassing the ability to code proficiently, comprehend control structures, and manipulate data structures effectively.
  • Understanding of Mathematics and Statistics: A firm grasp of core mathematical concepts such as algebra, calculus, probability, and statistics is indispensable for data analysis and modeling endeavors.
  • Familiarity with Linear Algebra: Knowledge of linear algebra proves advantageous for comprehending machine learning algorithms, particularly those involving matrix operations and linear transformations.
  • Database Proficiency: Competence in database management systems (DBMS) and SQL can facilitate accessing and querying data stored in relational databases.
  • Development of Critical Thinking and Problem-Solving Skills: Data science demands the ability to solve intricate problems and make decisions driven by data. Cultivating critical thinking and problem-solving abilities is indispensable for success in this field.

The Data Science course fees in OMR depend on the program level (basic, intermediate, or advanced) and the course format (online or in-person).On average, the Data Science course fees come in the range of 45,000-65,000 INR for 4 months, inclusive of international certification. For some of the most precise and up-to-date details on fees, duration, and data science certification courses in OMR, kindly contact our Data Science Training and Job Placement Institute in Chennai directly.

The following are some of the jobs related to data science:

  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Machine Learning Engineer
  • Business Intelligence (BI) Analyst
  • Data Architect

As per data from AmbitionBox, the data science/ data analysts freshers salary is approximately ₹3-4 lakhs annually. For mid-career analysts with 2-3 years of experience, the average annual salary is around ₹8 lakh. An experienced data science analyst with more than 4-5 years of experience can anticipate an average yearly salary of ₹13.4 lakhs.

Here are several real-time applications of data science:

  • Predictive Maintenance
  • Fraud Detection
  • Recommendation Systems
  • Dynamic Pricing
  • Supply Chain Optimization

Boost your skills with Data Science Course In OMR with Experts

Our Mentors are from Top Companies like:
  • Our data science institute in OMR has a lot of skilled data science trainers with experience in teaching students at all levels.
  • Our trainers are experienced in using R, Python, and Scala for data analysis, predictive modeling, and visualization.
  • Our trainers have expertise in machine learning algorithms like linear regression, logistic regression, random forests, gradient boosting, and clustering.
  • Our trainers are capable of developing predictive models using regression and classification techniques.
  • Our trainers are experienced in using SQL, NoSQL, and big data technologies for building data pipelines.
  • Our Data Science trainers are knowledgeable in tools like Tableau, Power BI, and Google Data Studio for creating business visualizations.
  • Our trainers can understand real-world data analysis scenarios and can interpret data effectively.
  • They can motivate students to apply data science techniques to business solutions.
  • Our trainers always stay updated with industry trends and recent developments in data science.
  • Our trainers can teach students to use the right tools for different tasks and deliver hands-on projects.
  • They are proficient in teaching the basics of statistics and mathematics with relevant problems.
  • Our trainers have very positive relationships with students and motivate them in assignments, resume-making, and interview preparation.
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Explore Real Projects Built by Softlogic Students

Explore the real-world, enterprise-level projects designed and executed by our successful alumni. Gain the same hands-on exposure to live industry projects, build a standout portfolio, and prove your practical skills to top hiring managers instantly.
Precision Agriculture
Environmental Surveillance
Cybersecurity Monitoring
Project Practices for Data Science Course In OMR

Why Softlogic Systems is the Best Choice for Data Science Course In OMR

Crowdfunding Platform
Online & Offline Training Options
Learn at your convenience with flexible classroom and live online training.
Learn from 100+ Real-Time Developers
Get trained by industry professionals with years of hands-on experience.
Voting System with Python Django
Hands-on Projects & Codeathons
Practice with real-time projects and coding challenges to build confidence.
0% EMI Fee Options
Pay your course fees flexibly with easy EMI plans at zero interest.
Resume & Interview Support
Get expert help with resume building, mock interviews, and soft skills.
Placement with Top IT Firms
Access placement opportunities with leading MNCs and IT companies.
500+ Hiring Partners
Benefit from Softlogic’s strong recruiter network for faster job placement.
No Backdoor Jobs
We ensure only genuine placement opportunities with trusted companies.
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Certifications

Earn Your Certificate of Completion
Validate your achievements with Softlogic's Certificate of Completion, verifying successful fulfillment of all essential components.
Take Your Career to the Next Level with  Certifications
Get a certifications through our training programs to gain a competitive edge in the industry.
Stand Out from the Crowd with Codethon Certificate
Verify the authenticity of your real-time projects with Softlogic's Codethon certificate.

The Global Edge: Certified Vs. Uncertified

With Global Certification

  • ✅ Visibility in ATS Filters
  • ✅Command Higher Hikes
  • ✅ Verified Skill Proof
  • ✅Global Job Access
  • ✅ Fast-Tracked Senior Roles

Without Global Certification

  • ❌ Stuck in Screening
  • ❌ Lower Starting Pay
  • ❌ Unproven Technical Ability
  • ❌ Limited Local Scope
  • ❌ Slower Career Leader
  •  
Let's take the First Step to a better future! GET Free Career Guidance

The SLA Way to Get Placed

Seven connected stages, one continuous path from enrollment to your career launch — here is exactly how it works, step by step.

01
🎓

Technical Mastery

Industry-aligned curriculum, daily coding tasks, real-time projects.

02
💻

Career Readiness

Aptitude, communication, ATS-friendly resume optimization.

03
🎯

Placement Prep

Aptitude, communication tuning and technical interview mastery.

04
💬

Panel Mock Interviews

Real company-pattern mocks with expert panel feedback.

05
👨‍💼

Interview Schedule

Multiple company drives with lifetime placement support.

06
📝

Interview Feedback

Detailed performance analysis and one-on-one guidance.

07
🏆

Launch Your Career

Land your first job with lifetime alumni and career support.

Listen to Our Students' Video Testimonials

FAQ

The Data Science course fees in OMR depend on the program level (basic, intermediate, or advanced) and the course format (online or in-person).On average, the Data Science course fees come in the range of 45,000-65,000 INR for 4 months. For some of the most precise and up-to-date details on fees, duration, and certified data science courses in OMR, kindly contact our Best Software training institute in OMR Chennai directly.

Yes, SLA does provide international certification, inclusive of the course offered.

Yes, SLA has a specially designated communications trainer who helps students develop their communication skills. 

Yes, SLA has a specially designated HR department that deals with student grievances and issues. 

Yes, SLA does provide students with study materials, project files, sample papers, interview questions and answers, etc. 

In general, although studying data science presents its challenges, it offers substantial rewards and finds application across various sectors. Through commitment, consistent practice, and ongoing learning, numerous individuals have achieved success in pursuing a career in this field. Moreover, in institutes like SLA, there are specialized trainers who are experts in teaching students of every level.

Essential tools and libraries for Data Science encompass NumPy, Pandas, scikit-learn, TensorFlow, Keras (for deep learning), Matplotlib, and Seaborn for data visualization. Becoming proficient with these resources empowers the efficient execution of diverse data analysis tasks.

A comprehensive Data Science curriculum should encompass fundamental concepts including data preprocessing, exploratory data analysis (EDA), statistical analysis, and machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and support vector machines. Additionally, familiarity with deep learning techniques and model evaluation methodologies is essential.

Proficiency in handling large datasets and performing analysis on big data necessitates understanding distributed computing frameworks like Apache Hadoop and Apache Spark. These frameworks enable efficient processing, analysis, and extraction of insights from vast datasets, a key aspect covered in data science courses.

Data preprocessing techniques such as data cleaning, feature scaling, addressing missing values, and feature engineering are crucial preparatory steps in Data Science. These techniques ensure data quality, consistency, and relevance, enhancing the efficacy and accuracy of subsequent analyses and modeling efforts emphasized in Data Science courses.

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