Data Science has transformative implications for the IT industry, empowering organizations to unlock the value of their data, drive innovation, and achieve their business objectives more effectively. It continues to revolutionize industries across sectors, driving growth, efficiency, and innovation in the digital age. So, now is the best time for you to start in our Data Science Online Training Institute. In our Data Science Online Course, you will be exposed to the new updated syllabus that is curated by experts from the IT industry, which gives our syllabus massive credibility. So, enroll in our Data Science Online Training with certification & placements to master Data Science from your home.
Data Science Online Training
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DURATION
4 to 8 months
Mode
Live Online / Offline
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What this Course Includes?
- Technology Training
- Aptitude Training
- Learn to Code (Codeathon)
- Real Time Projects
- Learn to Crack Interviews
- Panel Mock Interview
- Unlimited Interviews
- Life Long Placement Support
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Course Syllabus
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Breakdown of Data Science Online Training Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
January 2025
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
January 2025
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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Syllabus of Data Science Online Training
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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
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
Objectives of Learning Data Science Online Training
Download Syllabus
Our Data Science online training syllabus is curated and customized by some of our IT professionals while keeping in mind the current trends in the IT industry. Students will have the opportunity to learn a lot of interesting concepts like – control structures, loop functions, skewness, binomial distribution and more.
By the time students finish the course they will be completely well-versed in major data science topics like:
- Data Handling in R programming
- Statistics
- Probability
- Regression Techniques
- Market Basket Analysis
- Decision trees and more.
Reason to choose SLA for Data Science Online Training
- SLA stands out as the Exclusive Authorized Training and Testing partner in Tamil Nadu for leading tech giants including IBM, Microsoft, Cisco, Adobe, Autodesk, Meta, Apple, Tally, PMI, Unity, Intuit, IC3, ITS, ESB, and CSB ensuring globally recognized certification.
- Learn directly from a diverse team of 100+ real-time developers as trainers providing practical, hands-on experience.
- Instructor led Online and Offline Training. No recorded sessions.
- Gain practical Technology Training through Real-Time Projects.
- Best state of the art Infrastructure.
- Develop essential Aptitude, Communication skills, Soft skills, and Interview techniques alongside Technical Training.
- In addition to Monday to Friday Technical Training, Saturday sessions are arranged for Interview based assessments and exclusive doubt clarification.
- Engage in Codeathon events for live project experiences, gaining exposure to real-world IT environments.
- Placement Training on Resume building, LinkedIn profile creation and creating GitHub project Portfolios to become Job ready.
- Attend insightful Guest Lectures by IT industry experts, enriching your understanding of the field.
- Panel Mock Interviews
- Enjoy genuine placement support at no cost. No backdoor jobs at SLA.
- Unlimited Interview opportunities until you get placed.
- 1000+ hiring partners.
- Enjoy Lifelong placement support at no cost.
- SLA is the only training company having distinguished placement reviews on Google ensuring credibility and reliability.
- Enjoy affordable fees with 0% EMI options making quality training affordable to all.
Want to Speak to our Trainer about Data Science Online Training?Request a Free Callback
Highlights of The Data Science Online Training
What is Data Science?
Data science is an interdisciplinary domain focused on extracting insights and wisdom from both structured and unstructured data. It utilizes methodologies and principles from diverse disciplines including statistics, mathematics, computer science, and domain expertise to analyze data, detect patterns, forecast outcomes, and facilitate informed decision-making processes.
What are the reasons for learning Data Science?
Learning data science offers many compelling reasons:
- Lots of Jobs: Many industries need data scientists because they have a lot of data. Learning data science can lead to many job opportunities in areas like technology, finance, healthcare, retail, and more.
- Good Pay: Data scientists get paid well because they have special skills that are valuable to companies. Because there aren’t enough data scientists to go around, they often get offered competitive salaries.
- Useful Everywhere: Knowing data science can be helpful in lots of different industries and jobs. Whether you’re interested in analyzing business data, working with artificial intelligence, doing machine learning, or doing research, data science skills can be useful in many different areas.
- Helps Come Up with New Ideas: Data science helps companies use data to solve complicated problems, find trends, and make smart decisions. Learning data science can help you think of new ideas that make a real difference in companies and society.
What are the prerequisites for learning Data Science?
No prior prerequisites are necessary, but a basic understanding of math concepts like algebra, calculus, probability, and statistics is helpful. Learn programming in Python or R and use tools like NumPy, pandas, scikit-learn, and TensorFlow. Understand statistics and data visualization with Matplotlib, Seaborn, and ggplot2.
Our Data Science Course is suitable for:
- Students
- Job Seekers
- Freshers
- IT professionals aiming to enhance their skills
- Professionals seeking career change
- Enthusiastic programmers
What are the course fees and duration?
The Data Science course fees 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 1 to 1.4 Lakhs INR for 6 months, inclusive of international certification. For some of the most precise and up-to-date details on fees, duration, and certified Data Science certification, kindly contact our Best Placement Training Institute in Chennai directly.
What are some of the jobs related to Data Science?
The following are some of the jobs related to Data Science:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Engineer
- Business Intelligence Analyst
What is the salary range for the position of Data Scientist?
As per data from AmbitionBox, the Data Scientist freshers salary typically with less than 2 years of experience earn approximately ₹8-9 lakhs annually. For mid-career Data Scientist with around 4 years of experience, the average annual salary is around ₹14.1 lakhs. An experienced Data Scientist with more than 7 years of experience can anticipate an average yearly salary of around ₹19.7 lakhs. Visit SLA for more Data Science courses.
List a few real-time Data Science applications.
Here are several real-time Data Science applications:
- Fraud Detection
- Predictive maintenance
- Real-time recommendation
- Dynamic pricing
- Supply Chain optimization
Who are our Trainers for Data Science Online Training?
Our Mentors are from Top Companies like:
- Experienced and motivated Data Science Trainers, specialising in teaching Data Science concepts to students of all levels.
- They possess expertise in using programming languages like R, Python and Scala for data analysis, predictive modelling and visualisations.
- They have exceptional understanding of machine learning algorithms like Linear Regression, Logistic Regression, Random Forests, Gradient Boosting and clustering.
- They hold proven ability to develop predictive models using regression and classification techniques.
- They are xperienced in using SQL, NoSQL and big-data technologies for building data pipelines.
- They have detailed knowledge of using tools like Tableau, Power BI and Google Data Studio to create visualisations for business presentations.
- They have astute understanding of the real-world data analysis scenarios and the need for high-level data interpretation.
- They have the ability to motivate and encourage students to understand and apply Data Science techniques for business solutions in the data science training in Chennai.
- They are able to guide students with the fundamentals and the current trends of the industry and have demonstrated ability to stay abreast to the recent developments in the Data Science domain.
- They are adept in teaching students to use the right tools, industry-standard and open-source Data Science tools for the right tasks and are experienced in designing and delivery of hands-on Data Science projects and assignments.
- They are well-versed in teaching basics of statistics and mathematics with relevant problems.
- They are well versed in building strong and positive relationships with the students and is able to motivate them in complex tasks, assignments, resume making, and interview preparation.
Want to Speak to our Trainer about Data Science Online Training?Request a Free Callback
What Modes of Training are available for Data Science Online Training?
Offline / Classroom Training
A Personalized Learning Experience with Direct Trainer Engagement!
- Direct Interaction with the Trainer
- Clarify doubts then and there
- Airconditioned Premium Classrooms and Lab with all amenities
- Codeathon Practices
- Direct Aptitude Training
- Live Interview Skills Training
- Direct Panel Mock Interviews
- Campus Drives
- 100% Placement Support
Online Training
Instructor Led Live Training! Learn at the comfort of your home
- No Recorded Sessions
- Live Virtual Interaction with the Trainer
- Clarify doubts then and there virtually
- Live Virtual Interview Skills Training
- Live Virtual Aptitude Training
- Online Panel Mock Interviews
- 100% Placement Support
Corporate Training
Blended Delivery model (both Online and Offline as per Clients’ requirement)
- Industry endorsed Skilled Faculties
- Flexible Pricing Options
- Customized Syllabus
- 12X6 Assistance and Support
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Certifications
Take your career to new heights with Softlogic's software training certifications.
Improve your abilities to get access to rewarding possibilities
Improve your abilities to get access to rewarding possibilities
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 an IBM Certification
Get an IBM certification 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.
Project Practices for Data Science Online Training
Factory Maintenance Prediction
Factories use real-time data science to predict when machines might break down. Sensors in machines gather data on things like temperature and performance. Algorithms then analyze this data to predict breakdowns before they happen, reducing downtime and making production more efficient.
Health Monitoring Devices
Wearable health gadgets and mobile apps use real-time data science to track vital signs like heart rate and blood pressure. Algorithms analyze this info instantly, offering personalized health tips, spotting issues, and alerting users or doctors to possible health problems.
Emergency Response
Emergency services use real-time data science to coordinate disaster responses. Sensors and satellites provide real-time data on disasters, while algorithms predict impacts and allocate resources. This ensures timely responses, minimizing damage and saving lives.
Stock Market Prediction
Traders use real-time data science to predict stock market movements. Algorithms analyze real-time market data and social media sentiment to identify trends and signals. This helps traders make informed decisions and maximize returns on investments.
Smart Home Systems
Smart homes use real-time data science to automate tasks and save energy. Sensors track occupancy and temperature, while algorithms adjust heating, cooling, and lighting accordingly. This improves comfort and security while reducing energy waste.
Health Monitoring Devices
Wearable health devices use real-time data science to monitor vital signs. Sensors track metrics like heart rate and blood glucose levels. Algorithms analyze this data to detect health issues and send alerts to users and doctors, enabling prompt medical intervention.
Energy Trading
Energy companies use real-time data science to optimize energy trading. By analyzing data from energy markets and weather forecasts, they predict supply and demand changes. This helps them adjust trading strategies in real-time, maximizing profits and ensuring a stable energy market.
Traffic Management
Cities use real-time data science to manage traffic flow. Sensors and GPS data from vehicles give instant updates on traffic conditions. Algorithms analyze this data to predict traffic patterns and suggest alternative routes, reducing congestion and travel times.
Aircraft Maintenance
Airlines and manufacturers use real-time data science to predict when airplane parts might fail. Sensors on planes constantly monitor engine performance and other factors. Data analysis helps predict potential problems early, ensuring safety and reducing downtime.
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FAQs for
Data Science Online Training
What is the importance of data visualization in Data Science?
1.
Data visualization is an important step in data science. It helps to quickly and easily explore and understand the data, identify patterns in the data and find relationships between variables.
What are the different techniques used for data pre-processing?
2.
The different techniques used for data pre-processing include normalization, imputation, binning, scaling, outlier detection and treatment.
What is the purpose of exploratory data analysis?
3.
Exploratory data analysis (EDA) is an iterative process used to analyze data in order to summarize their main characteristics, uncover relationships between variables, and identify outliers and anomalies.
How is supervised learning different from unsupervised learning?
4.
In supervised learning, models are trained on a set of labeled data and learn from it to make predictions, while in unsupervised learning, models find patterns and relationships from datasets without labels to generate insights.
What is the difference between a decision tree and a random forest?
5.
A decision tree is a type of supervised machine learning algorithm which creates a tree-like structure to predict the value of a target variable by learning simple decision rules inferred from the data. A random forest is an ensemble technique that combines multiple decision trees to produce more accurate and stable predictions than a single decision tree.
Which organisations are actively looking to hire employees with expertise in Data Science?
6.
Professionals who are skilled in data science are in high demand across a wide range of organizations, including those of Google, Microsoft, Deloitte Accenture, IBM, Capgemini, Amazon, Apple, and many more MNCs.
What makes Softlogic Systems a good place to study?
7.
- At Softlogic Systems, you will receive hands-on experience and rigorous training from industry professionals. The course content covers both introductory and advanced-level material.
- Learning from Data Science training in Chennai, which combines excellence and innovation, will provide you with abilities and expertise that are unique and essential in both your personal and professional lives.
- When compared to competitors in the software training market, our placement services are unparalleled.
How does the Softlogics Placement team support us?
8.
The placement support offered by Softlogic increases your chances of getting the job of your dreams. Certified students who are interested in making a career change or entering the workforce for the first time will have access to comprehensive assistance through our placement assistance. Softlogic will provide the following premium services as part of our placement assistance:
- Resume building
- Career Guidance and Advising
- Interview practise sessions
- Career Expos
Does the Institute offer any certification after completing the Data Science training in Chennai?
9.
Yes, once you complete the training succesfully, you will be awarded the Softlogic Systems Data Science Training Certification that is accredited by IBM.
Does the Institute provide access to additional resources for the students during the Data Science training in Chennai?
10.
Yes, the Institute will provide project files and study materials for reference, sample papers, interview questions, and extra help when needed.
Additional Information for
Data Science Online Training
Our Data Science Training in Online has the best curriculum among other IT institutes ever. Our institute is located in the hub of IT companies, which creates abundance of opportunities for candidates.. Our Data Science course syllabus will teach you topics that no other institute will teach. Enroll in our Data Science training to explore some innovative Top project ideas for the Data Science.
1.
Augmented Analytics with Machine Learning and AI
Augmented analytics, using machine learning and AI, is making data analysis easier. It integrates natural language processing (NLP) and automated insights, simplifying data extraction for all users, not just tech experts.
2.
Continuous Intelligence for Real-Time Decision Making
Continuous intelligence uses real-time data for quick decision-making. Businesses are using data to respond rapidly to changes. This trend involves real-time data processing and integration with business processes, improving operations and decision-making.
3.
Data-Driven Solutions for Global Challenges
Data is increasingly used to tackle global issues like climate change and poverty. More innovative data applications are expected to have a positive impact on the world. These trends show how data science is evolving and the importance of using data for informed decisions and addressing complex challenges.
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