Data Science plays a crucial role in extracting insights, solving complex problems, and driving innovation across various industries and domains. So enroll in our Data Science Training Institute in OMR to learn from our experienced trainers, who will make the understanding of topics very easy. Our Data Science Course in OMR also has the best syllabus that was curated by professionals from the IT industry. This gives our Data Science syllabus a massive credibility. So, enroll in our Data Science Training with certification & placements to enjoy the privileges that we have to offer.
Data Science Training in OMR
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DURATION
6 Months
Mode
Live Online / Offline
EMI
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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 Course Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
September 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
September 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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Syllabus for The Data Science Course
Download Syllabus
Introduction
1
- 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
2
- 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
3
- 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
4
- 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
5
- 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
6
- 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
7
- 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
8
- 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
9
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
10
- 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
11
- 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)
12
- 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
13
- 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
14
- 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
15
- 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
16
- Clustering using Tableau
- Time series analysis using Tableau
- Simple Linear Regression using Tableau
R integration in Tableau
17
- 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 Course
Download Syllabus
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.
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.
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Highlights of The Data Science Course
What is data science?
1.
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.
What are the reasons for learning data science?
2.
- 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.
What are the prerequisites for learning the Data Science course in OMR?
3.
- 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.
What are the course fees and duration?
4.
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.
What are some of the jobs related to data science?
5.
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
What is the salary range for the position of data science/ data analyst?
6.
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.
List a few real-time data science applications.
7.
Here are several real-time applications of data science:
- Predictive Maintenance
- Fraud Detection
- Recommendation Systems
- Dynamic Pricing
- Supply Chain Optimization
Who are our Trainers for The Data Science Course?
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.
Want to Speak to a Trainer about Data Science?Request a Free Callback
What Modes of Training are available for Data Science?
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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Project Practices for The Data Science Course
Precision Agriculture
Farmers leverage real-time data obtained from sensors, drones, and satellite imagery to monitor crop conditions, optimize irrigation practices, and manage pest control, resulting in improved crop yields and minimized environmental impact.
Environmental Surveillance
Environmental agencies gather real-time data from sensors and satellite imagery to monitor air and water quality, alongside other environmental factors, facilitating early detection of pollution and environmental risks.
Cybersecurity Monitoring
Security experts employ real-time data analysis and machine learning algorithms to detect and counteract cybersecurity threats, including malware attacks, unauthorized intrusions, and data breaches.
Proactive Healthcare Management
Hospitals and healthcare institutions utilize real-time patient data and machine learning models to forecast disease outbreaks, identify high-risk patients, and allocate resources effectively.
Logistics Optimization
Transportation companies utilize real-time data analytics to monitor vehicle locations, and fuel consumption, and optimize routes, as well as manage maintenance schedules efficiently.
E-commerce Personalization
Online retailers employ immediate data analysis to tailor product recommendations, promotional offers, and marketing communications to match the unique preferences and browsing habits of individual customers.
Weather Forecasting
Meteorologists leverage up-to-date information obtained from weather stations, satellites, and radar systems to produce precise and timely weather predictions, aiding individuals and businesses in preparing for adverse weather conditions.
Traffic Management
Government transportation agencies utilize real-time data gathered from traffic sensors, cameras, and GPS devices to oversee traffic patterns, optimize signal timing, and promptly identify incidents like accidents or congestion.
Social Media Analytics
Social media platforms analyze ongoing user interactions and behaviors to offer customized suggestions, targeted advertisements, and real-time insights into popular discussions and topics.
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FAQs for
The Data Science Course
What is the cost of Data Science training in OMR?
1.
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.
Does SLA provide international certification, inclusive of the course?
2.
Yes, SLA does provide international certification, inclusive of the course offered.
What are the other skills the SLA coaches provide along with the courses?
3.
Yes, SLA has a specially designated communications trainer who helps students develop their communication skills.
Does SLA have a forum to address student grievances?
4.
Yes, SLA has a specially designated HR department that deals with student grievances and issues.
Does SLA provide any extra resources for students?
5.
Yes, SLA does provide students with study materials, project files, sample papers, interview questions and answers, etc.
How hard is Data Science?
6.
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.
What are the indispensable libraries and tools in Data Science that I should acquaint myself with?
7.
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.
Which core concepts and algorithms should I prioritize learning in a Data Science course?
8.
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.
How can I manage extensive datasets and analyze big data during a data science course?
9.
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.
What are the typical data preprocessing methods, and why are they pivotal in data science?
10.
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.
Additional Information for
The Data Science Course
Our Data Science Training in OMR 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.
Rising Demand
Organizations across various industries increasingly value data-driven decision-making, leading to a growing need for Data Scientists and Data Analysts. This demand stems from the desire to extract actionable insights from complex datasets for a competitive advantage.
2.
Advancements in AI and ML
Continued progress in artificial intelligence (AI) and machine learning (ML) will see Data Science playing a central role in the development and deployment of intelligent systems. This includes applications such as natural language processing, computer vision, predictive analytics, and autonomous technologies.
3.
Big Data Technologies
The widespread adoption of big data technologies like Apache Hadoop, Apache Spark, and cloud-based data storage solutions facilitates more efficient storage, processing, and analysis of vast datasets. Data Scientists adept in utilizing these technologies will be sought after to derive meaningful insights from large-scale data.
4.
Expansion into New Fields
Data Science is extending beyond traditional sectors like finance and healthcare into emerging domains such as cybersecurity, IoT (Internet of Things), and personalized medicine. This diversification presents opportunities for Data Scientists to tackle challenges in evolving fields.
5.
Ethical Considerations and Compliance
With the increasing prevalence of data usage, there’s a growing focus on ethical considerations and regulatory compliance regarding data privacy, security, and transparency. Data Scientists must navigate these ethical and regulatory frameworks while ensuring responsible data utilization.
6.
Collaboration Across Disciplines
Data Science inherently involves interdisciplinary collaboration, requiring cooperation between Data Scientists, domain experts, software engineers, and other stakeholders. Future endeavors in Data Science will emphasize fostering collaboration across diverse teams to address complex issues and foster innovation.
7.
Continuous Learning and Development
Given the rapid evolution of technology and methodologies in Data Science, professionals in the field must embrace continuous learning and skill enhancement. Staying updated on the latest trends, tools, and best practices is crucial for staying competitive and driving innovation in Data Science and related fields.