Data Science has several uses in the IT industry ranging from Fraud Detection, Network optimization, Code Quality Analysis and more, that is why Data Science is highly sought-after in IT now. Our Data Science Training Institute comes with the most up-to-date syllabus and modern infrastructure, along with experienced trainers as well. Therefore, our Data Science Course will give students a holistic learning of Data Science, which will eventually give them a prolonged, high-paying career in Data Science as Data Scientist and so on. So go ahead and explore more down below to get all the information you need about our Data Science Course with certification & placements.
Data Science Training
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3 Months
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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 Schedules
Course Syllabus
Course Fees
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Breakdown of Data Science Training Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
April 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
April 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 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 Training
Download Syllabus
The Data Science Training will cover all the topics ranging from fundamental to advanced concepts, which will make it easy for students to grasp Data Science. The Data Science Course Curriculum is composed of some of the most useful and rare concepts that will surely give students a complete understanding of Data Science as well. So, some of those curriculum are discussed below as objectives:
- To make students well-versed with fundamental concepts of Data Science like – Introduction to Business Analytics, Basic Operations in R Programming, Introduction to Statistics etc.
- To make students more aware of Data Science by making them learn concepts like – Data Visualization Techniques, Machine Learning, Regression Techniques, Market Basket Analysis etc.
- To make students knowledgeable in advanced Data Science like – Time Series Analysis, Decision Trees using R, Tableau Analytics, R Integration in Tableau etc.
Reason to choose SLA for Data Science 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 Training?Request a Free Callback
Highlights of The Data Science Training
What is Data Science?
Data science integrates various techniques and methods to derive insights from data. It involves collecting, cleaning, and analyzing data, using statistical and machine learning methods, and creating visual representations. Key objectives include predicting trends, summarizing past data, recommending actions, and understanding causes. Applied across sectors like healthcare, finance, and retail, data science drives decision-making and solves complex problems.
What is Data Science Full Stack?
A Data Science Full Stack encompasses all tools, techniques, and technologies needed for a complete data science project, from data acquisition to application deployment. It includes data collection, storage, cleaning, and preprocessing, as well as exploratory analysis, statistical modeling, machine learning, and visualization. Additionally, it covers big data technologies, deployment, and ongoing monitoring to ensure a comprehensive data workflow.
What are the reasons for learning Data Science?
The following are the reasons for learning Data Science:
- Job Market: Skills in data science are highly sought after in various industries such as technology, finance, healthcare, and marketing.
- Diverse Roles: Career paths vary from data analyst to machine learning engineer, providing multiple job options.
- Informed Choices: Data science helps organizations make evidence-based decisions, enhancing efficiency and strategic planning.
- Competitive Edge: Businesses use data insights to gain a market advantage.
What are the prerequisites for learning Data Science?
The following are the prerequisites for learning Data Science:
- Statistics: Knowledge of statistical methods, including probability, distributions, hypothesis testing, and regression.
- Python: Proficiency in Python, a key language in data science due to libraries like Pandas, NumPy, and Scikit-learn.
- Data Manipulation: Skills in data cleaning and manipulation with tools such as Pandas or Excel.
- Relational Databases: Knowledge of SQL and database concepts like tables, joins, and indexing.
What are the course fees and duration?
Our Data Science Course Fees may vary depending on the specific course program you choose (basic / intermediate / full stack), course duration, and course format (remote or in-person). On an average the Data Science Course Fees range from 30k to 40k, for a duration of 3 months with international certification based on the above factors.
What are some of the jobs related to Data Science?
The following are the jobs related to Data Science:
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning Engineer
- Business Intelligence Analyst
- Quantitative Analyst
- Data Architect
- Research Scientist
List a few real time Data Science applications.
The following are the Data Science real-time applications:
- Fraud Detection
- Recommendation System
- Predictive Maintenance
- Dynamic Pricing
- Real-time Analytics and Dashboard
- Traffic Management and Navigation
- Social Media Monitoring
Who are our Trainers for Data Science Training?
Our Mentors are from Top Companies like:
- Experienced and dedicated Data Science trainers excel in teaching students across all skill levels about Data Science principles.
- They are skilled in programming languages such as R, Python, and Scala, which they use for data analysis, predictive modeling, and creating visualizations.
- Their expertise includes a thorough understanding of machine learning algorithms like Linear Regression, Logistic Regression, Random Forests, Gradient Boosting, and various clustering techniques.
- They are adept at building predictive models using both regression and classification methods.
- They have practical experience with SQL, NoSQL, and big-data technologies, enabling them to develop powerful data pipelines.
- They effectively use visualization tools such as Tableau, Power BI, and Google Data Studio to create engaging visual representations for business insights.
- They possess a deep comprehension of real-world data analysis scenarios and emphasize the importance of advanced data interpretation.
- They motivate and guide students in applying Data Science techniques to tackle business challenges, especially within Data Science Training.
- They stay current with industry trends and advancements, continually updating their knowledge and teaching methods.
- They instruct students in using both industry-standard and open-source tools for various Data Science tasks, focusing on practical projects and assignments.
- They are proficient in teaching core statistics and mathematics concepts through hands-on problem-solving approaches.
- They foster strong, supportive relationships with students, providing motivation and assistance in complex tasks, assignments, resume building, and interview preparation.
Want to Speak to our Trainer about Data Science Training?Request a Free Callback
What Modes of Training are available for Data Science 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 Training
Dynamic Pricing Model
Create a model to adjust prices dynamically based on demand, competition, and other factors.
Churn Prediction Model
Develop a model to predict customer churn based on usage patterns, service interactions, and other features.
Image Classification with Deep Learning
Train a deep learning model to classify images into categories, such as identifying objects in photographs.
Recommendation System
Build a recommendation engine for products, movies, or content based on user preferences and behavior.
Real-Time Traffic Prediction
Build a model to forecast traffic conditions and optimize routes using real-time data from sensors or GPS.
Fraud Detection System
Develop a system to identify fraudulent transactions by analyzing patterns and anomalies in financial data.
Social Media Sentiment Analysis
Assess sentiment from social media posts or reviews to gauge public opinion about a brand, product, or event.
Sales Forecasting with Predictive Analytics
Create models to predict future sales using historical data, seasonal patterns, and economic indicators.
Customer Segmentation
Implement clustering methods, such as K-means, to group customers based on their purchasing behaviors, demographics, or other significant features.
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Interview Skills
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FAQs for
Data Science Training
Which programming languages are most commonly used in Data Science, and what are their advantages?
1.
Python and R are the main programming languages used in Data Science. Python is popular due to its ease of use, rich ecosystem of libraries (like Pandas, NumPy, and Scikit-learn), and flexibility. R is recognized for its robust statistical analysis features and is widely used in academic and research settings.
What methods are used to handle missing data in datasets?
2.
Missing data can be managed through various approaches, including imputation (substituting missing values with mean, median, or mode), utilizing algorithms designed to handle missing data, or by excluding records or features with missing values. The choice of method depends on the extent and nature of the missing data.
Could you describe how supervised learning differs from unsupervised learning?
3.
Supervised learning involves training models on datasets with labeled responses, aiming to predict outcomes for new, unseen data. In contrast, unsupervised learning involves working with unlabeled data to uncover hidden patterns or groupings.
What is feature engineering, and why is it significant?
4.
Feature engineering is the process of creating or modifying features to enhance a machine learning model’s performance. It is vital because the effectiveness of features directly influences the accuracy and efficiency of the model.
What evaluation metrics are typically used for classification and regression tasks?
5.
For classification tasks, metrics such as accuracy, precision, recall, F1 score, and ROC-AUC are commonly used. For regression tasks, metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared are often employed.
How is the effectiveness of a machine learning model assessed?
6.
To evaluate model performance, relevant metrics (for classification or regression) are used, along with cross-validation to ensure the model generalizes well to new data. Additionally, examining residuals or errors can help understand the model’s limitations.
What is the role of hyperparameter tuning in machine learning?
7.
Hyperparameter tuning involves adjusting the settings of a machine learning algorithm that are not learned from the data but are predefined before training (such as learning rate or the number of trees in a forest). The objective is to identify the optimal set of hyperparameters to enhance model performance.
How do big data technologies like Hadoop and Spark assist with Data Science tasks?
8.
Big data technologies such as Hadoop and Spark are designed to handle and analyze large-scale datasets efficiently. Hadoop provides a framework for distributed storage and processing, while Spark offers rapid, in-memory data processing capabilities, which are crucial for managing extensive data volumes.
Where is the corporate office of the Softlogic Systems located?
9.
The corporate office of the Softlogic Systems is located at K.K.Nagar.
What payment methods does Softlogic accept?
10.
Softlogic accepts a wide range of payment methods, including:
- Cash
- Debit cards
- Credit cards (MasterCard, Visa, Maestro)
- Net banking
- UPI
- Including EMI
Additional Information for
Data Science Training
1.
Scopes available in the future for learning Data Science
The following are the scopes available in the future for learning the Data Science Course:
- Deep Learning: Gaining expertise in neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for applications like image and speech recognition.
- Reinforcement Learning: Exploring reinforcement learning for use in robotics and autonomous systems.
- Cloud Services: Mastering cloud-based solutions (e.g., AWS, Google Cloud, Azure) for scalable data storage and processing.
- Distributed Computing: Acquiring advanced skills in distributed frameworks like Apache Spark and Hadoop to manage extensive datasets.
- Healthcare: Analyzing healthcare data to enhance personalized medicine, patient outcomes, and operational efficiency.
- Finance: Applying data science to financial forecasting, fraud detection, and risk management.
- Text Analysis: Utilizing advanced NLP techniques for sentiment analysis, entity recognition, and text generation.
- Interactive Dashboards: Designing dynamic dashboards with tools such as Tableau, Power BI, and D3.js.
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