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Course Highlights and Why Data Science Course in Chennai at FITA Academy?
FITA Academy offers a comprehensive Data Science Course that combines theoretical concepts with practical applications. Elaborate courseware covering various aspects such as Python/R with Data Science. Students can build models using Data Science algorithms, analyze datasets from real-world environments, and create interactive visualizations to practice what they learn. The Data Science Training in Chennai has flexible batch timings that allow us to provide classes to students on weekends, weekdays, and also provide fast-track sessions. FITA Academy has over 3000+ partners with high demand for Data Scientists, Data Analysts and many other roles related to the field, with whom we are collaborating to provide placement support. FITA Academy provides 100% Placement Assistance to all students who successfully complete the training. Upcoming Batches
- 21-11-2024
- Weekdays
- Thursday (Monday - Friday)
- 23-11-2024
- Weekend
- Saturday (Saturday - Sunday)
- 25-11-2024
- Weekdays
- Monday (Monday - Friday)
- 30-11-2024
- Weekend
- Saturday (Saturday - Sunday)
Classroom Training
- Get trained by Industry Experts via Classroom Training at any of the FITA Academy branches near you
- Why Wait? Jump Start your Career by taking Data Science Course in Chennai!
Instructor-Led Live Online Training
- Take-up Instructor-led Live Online Training. Get the Recorded Videos of each session.
- Travelling is a Constraint? Jump Start your Career by taking the Data Science Training Online!
Data Science Course Objectives
- Get to know the basics of Data Science, going through the introduction of concepts like Data Science Life Cycle.
- Getting introduced to Python or R programming, its versions and an in depth study of the programming languages. Students get to choose between either of the programming languages.
- Get to know about Fundamental Statistics, the uses and advantages of Statistical Analysis, and understanding Inferential and Descriptive Statistics.
- Understanding Data Science Models and Algorithms such as Predictive Model, Predictive Analysis, Linear Regression, Polynomial Regression etc.
- Getting to know Supervised and Unsupervised Learning Algorithms, Hypothesis Testing and Reinforcement Learning Algorithms.
Data Science Course Syllabus
Introduction to Data Science
- Understanding Data Science
- The Data Science Life Cycle
- Understanding Artificial Intelligence (AI)
- Overview of Implementation of Artificial Intelligence
- Machine Learning
- Deep Learning
- Artificial Neural Networks (ANN)
- Natural Language Processing (NLP)
- How R connected to Machine Learning
- R - as a tool for Machine Learning Implementation
Data Science with Python: Introduction to Python
- What is Python and history of Python
- Python-2 and Python-3 differences
- Install Python and Environment Setup
- Python Identifiers, Keywords and Indentation
- Comments and document interlude in Python
- Command line arguments and Getting User Input
- Python Basic Data Types and Variables
List, Ranges & Tuples in Python
- Understanding Lists in Python
- Understanding Iterators
- Generators, Comprehensions and Lambda Expressions
- Understanding and using Ranges
Python Dictionaries and Sets
- Introduction to the section
- Python Dictionaries and More on Dictionaries
- Sets and Python Sets Examples
Input and Output in Python
- Reading and writing text files
- Appending to Files
- Writing Binary Files Manually and using Pickle Module
Python functions
- Python user defined functions
- Python packages functions
- The anonymous Functions
- Loops and statement in Python
- Python Modules & Packages
Python Exceptions Handling
- What is Exception?
- Handling an exception
- try….except…else
- try-finally clause
- Argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions
Python Regular Expressions
- What are regular expressions?
- The match Function and the Search Function
- Matching vs Searching
- Search and Replace
- Extended Regular Expressions and Wildcard
Useful additions
- Collections – named tuples, default dicts
- Debugging and breakpoints, Using IDEs
Data Manipulation using Python
- Understanding different types of Data
- Understanding Data Extraction
- Managing Raw and Processed Data
- Wrangling Data using Python
- Using Mean, Median and Mode
- Variation and Standard Deviation
- Probability Density and Mass Functions
- Understanding Conditional Probability
- Exploratory Data Analysis (EDA)
- Working with Numpy, Scipy and Pandas
Understanding Machine Learning Models
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
More on Models
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi Level Models
- Selecting the Right Model or Model Selection
- Need for selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Exploring Supervised Learning Algorithms
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbour
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
Exploring Un-Supervised Learning Algorithms
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
Overview Reinforcement Learning Algorithm
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration Vs Exploitation tradeoff
Data Science with R: Introduction to R Programming
- What is R?
- History and Features of R
- Introduction to R Studio
- Installing R and Environment Setup
- Command Prompt
- Understanding R programming Syntax
- Understanding R Script Files
R Programming Basics
- Data types in R
- Creating and Managing Variables
- Understanding Operators
- Assignment Operators
- Arithmetic Operators
- Relational and Logical Operators
- Other Operators
- Understanding and using Decision Making Statements
- The IF Statement
- The IF…ELSE statement
- Switch Statement
- Understanding Loops and Loop Control
- Repeat Loop
- While Loop
- For Loop
- Controlling Loops with Break and Next Statements
More on Data Types
- Understanding the Vector Data type
- Introduction to Vector Data type
- Types of Vectors
- Creating Vectors and Vectors with Multiple Elements
- Accessing Vector Elements
- Understanding Arrays in R
- Introduction to Arrays in R
- Creating Arrays
- Naming the Array Rows and Columns
- Accessing and manipulating Array Elements
- Understanding the Matrices in R
- Introduction to Matrices in R
- Creating Matrices
- Accessing Elements of Matrices
- Performing various computations using Matrices
- Understanding the List in R
- Understanding and Creating List
- Naming the Elements of a List
- Accessing the List Elements
- Merging different Lists
- Manipulating the List Elements
- Converting Lists to Vectors
- Understanding and Working with Factors
- Creating Factors
- Data frame and Factors
- Generating Factor Levels
- Changing the Order of Levels
- Understanding Data Frames
- Creating Data Frames
- Matrix Vs Data Frames
- Sub setting data from a Data Frame
- Manipulating Data from a Data Frame
- Joining Columns and Rows in a Data Frame
- Merging Data Frames
- Converting Data Types using Various Functions
- Checking the Data Type using Various Functions
Functions in R
- Understanding Functions in R
- Definition of a Function and its Components
- Understanding Built in Functions
- Character/String Functions
- Numerical and Statistical Functions
- Date and Time Functions
- Understanding User Defined Functions (UDF)
- Creating a User Defined Function
- Calling a Function
- Understanding Lazy Evaluation of Functions
Working with External Data
- Understanding External Data
- Understanding R Data Interfaces
- Working with Text Files
- Working with CSV Files
- Understanding Verify and Load for Excel Files
- Using WriteBin() and ReadBin() to manipulate Binary Files
- Understanding the RMySQL Package to Connect and Manage MySQL Databases
Data Visualization with R
- What is Data Visualization
- Understanding R Libraries for Charts and Graphs
- Using Charts and Graphs for Data Visualizations
- Exploring Various Chart and Graph Types
- Pie Charts and Bar Charts
- Box Plots and Scatter Plots
- Histograms and Line Graphs
Exploring Statistical Computations using R
- Understanding the Basics of Statistical Analysis
- Uses and Advantages of Statistical Analysis
- Understanding and using Mean, Median and Mode
- Understanding and using Linear, Multiple and Logical Regressions
- Generating Normal and Binomial Distributions
- Understanding Inferential Statistics
- Understanding Descriptive Statistics and Measure of Central Tendency
Packages in R
- Understanding Packages
- Installing and Loading Packages
- Managing Packages
Understanding Machine Learning Models
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
More on Models
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi Level Models
- Selecting the Right Model or Model Selection
- Need for selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Exploring Supervised Learning Algorithms
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbor
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
Exploring Un-Supervised Learning Algorithms
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
Overview Reinforcement Learning Algorithm
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration Vs Exploitation tradeoff
Data Science Course Trainer Profile
- Trainers at FITA Academy have more than 10+ years of experience in various fields of Data Science such as Data Analysis, Data Analytics, Data Engineering, etc.
- They are certified professionals with extensive training and tutoring experience as well.
- In order to gain industry experience, the trainers at FITA Academy’s Data Science Training Institute In Chennai provide detailed hands-on training and have the students work on real-time projects during training.
- Students are trained by the trainers on how to make use of the latest algorithms and tools used in data science, as well as the methods.
- Regular recap sessions are conducted to ensure students keep track of what they are learning.
- At FITA Academy, trainers guide students with the necessary interview tips & support in building up a successful resume as part of their training.
- Students are guided by trainers in enhancing their technical skills in Data Science so that they can excel in the field.
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Student Success Story of Data Science Course in Chennai
Anitha, is a mother of a two year old girl. She graduated 5 years ago and worked for a year before quitting following marriage. She was thinking of resuming her career after financial responsibilities started to increase after the birth of her child. Her job search efforts were futile due to her long career break and lack of update in skillsets. That’s when she approached FITA Academy, after being referred by one of her cousins.
She opted for the Data Science Course in Chennai owing to her experience working in Spreadsheets and Databases. She opted for an online course due to her commitments with her child.
Despite the challenges she faced balancing family responsibilities and her classes, she did remarkably well attending the classes, completing the Capstone projects.
With our placement cell, she obtained training for placements and interviews. With her updated knowledge, she aced the interviews easily this time around. She got placed in 3 companies namely Team Everest, Accenture and Foyer Technologies. She opted for Accenture, where she got the offer as a Data Engineer for a salary of 6 LPA. Anita was able to start her second innings of her career with the help of FITA Academy.
Features of Data Science Course in Chennai at FITA Academy
Real-Time Experts as Trainers
At FITA Academy, you will learn from industry experts eager to share their knowledge with learners. You will also get personally mentored by the Experts.
LIVE Project
Get the opportunity to work on real-time projects that will provide you with deep experience. Showcase your project experience and increase your chances of getting hired!
Certification
FITA Academy offers certification. Also, get ready to clear global certifications. 72% of FITA Academy students appear for global certifications and 100% of them clear it.
Affordable Fees
At FITA Academy, the course fee is not only affordable, but you can also pay it in installments. Quality training at an affordable price is our motto.
Flexibility
At FITA Academy, you get the ultimate flexibility. Classroom or online training? Early morning or late evening? Weekday or weekend? Regular Pace or Fast Track? - Choose whatever suits you best.
Placement Support
Tied-up & signed MOUs with over 3000+ small & medium-sized companies to support you with opportunities to kick-start & advance your career.
Why Learn Data Science Course in Chennai at FITA Academy?
Live Capstone Projects
Real time Industry Experts as Trainers
Placement Support till you get your Dream Job offer!
Free Interview Clearing Workshops
Free Resume Preparation & Aptitude Workshops
Data Science Certification Training in Chennai
Upon completion of the Data Science Course in Chennai, students are provided with a certificate of completion. The certificate acknowledges the ability of the candidate to acquire total subject knowledge in addition to learning all the basic tools and algorithms that are utilized by Data Science professionals. Having this certification will help the student get the best job opportunities in MNCs. They can make a positive impression on the interviewer and increase their chances of getting a job. The Data Science Certification in Chennai helps students gain a thorough understanding of the major services in this Data science field.
Besides the one provided by FITA Academy, there are several certifications available for Data Science which are internationally recognized. Some of them are
- Google Professional Data Engineer Certification
- Microsoft Certified Azure Data Scientist Associate Certification
- Dell EMC Data Science Certification
- IBM Data Science Professional Certification
- SAS Certified Data Scientist
FITA Academy’s Data Science Course in Chennai helps students prepare for the above certifications.
Have Queries? Talk to our Career Counselor for more Guidance on picking the
right Career for you!
Placement Session & Job Opportunities after completing Data Science Course in Chennai
With the development of applications incorporating Big data and artificial intelligence, the demand for data science is growing at an unprecedented rate. To determine which series to produce in the future, P&G generates time series models of the demand for their products using data science. In contrast, Netflix uses data science to understand the viewing patterns of the audience in order to determine which shows to produce in the future.
Managing large amounts of data flowing into social media and e-commerce sites requires the expertise of data scientists. The majority of companies also consider data scientists to be the right path to embracing Artificial Intelligence. With the increasing demand for Data Science and Artificial Intelligence courses in Chennai, these skills are highly sought after. The combination of all these factors is projected to lead to an increase in the number of data science jobs in the next year, which is about 30% higher than the year before. FITA Academy understands this very well and that is the reason we give utmost importance to training students in gaining practical experience and for landing jobs through the data science course in chennai with placement. “100% placement training and assistance is provided to students at the end of the course.”
What are the challenges of getting a data scientist job if data science is in demand?
Competency is a keyword to be kept in mind if you wish to be hired as a data scientist. With the increasing demand for data scientists, companies are in search of candidates with exceptional skills in data science.
A data scientist should have sound analytical skills, technical skills to perform tasks using various tools and techniques, programming ability, knowledge of statistics and understanding of the business. FITA Academy ensures you get those skills through the Data Science Course in Chennai.
As a Data Scientist, you need to find the important aspects of data using math and statistics skills, correlate and find the linkage between different sets of data, develop models with the data using programming languages like Python or R and provide valuable business insights or strategies for the company. Possessing exceptional knowledge in statistics without sufficient programming skills or a clear understanding of the business leads nowhere close to becoming a data scientist.
Though most companies hire freshers from IITs, aspiring candidates from any university with expertise in skill sets can become data scientists. The Data Science Course in Chennai, provided by FITA Academy, helps you acquire the desired skill sets to land your dream career as a Data Scientist.
Anyone willing to become a data scientist can acquire and develop the above mentioned skills by joining the Data Science Classes In Chennai, provided by FITA Academy. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their career as a data scientist.
Also Read: Data Scientist Salary For Freshers
What are the differences between Data scientist vs Data Analyst vs data engineer?
Data science has become the most prominent word in recruitment sites due to its demand in various organizations around the world. You could have noticed various designations like Data Scientists, Data Analyst, Data Engineer, and various other terms also. Some people tend to think that these terms are synonymous and use them interchangeably. Although, all the three roles involve the usage of data, let us discuss the differences among Data Scientist, Data Analyst and Data Engineer.
The key difference lies in the various tasks they perform using the data.
Data Analyst: Data Analysts add value to the organization by utilizing the data to answer questions and arrive at better solutions for business problems. This is the role predominantly given to entry-level professionals in the Data Science field. Typical tasks of a Data Analyst consist of data cleaning and creating visualizations of the findings, helping the company make better data-driven decisions.
Data Scientist: Data Scientists use their expertise in statistics and develop Machine Learning models to make predictive analyses and answer vital business problems. Data scientists unfold business insights from the data using supervised or unsupervised learning methods in their ML models. Data scientists train their mathematical models to identify better patterns to predict business trends accurately. The key difference between a Data Analyst and a Data Scientist is that a Data scientist provides a whole new approach to understanding data and builds models for new questions. In contrast, a Data Analyst analyzes recent trends using the data and converts the results for key business decisions.
Data Engineer: Data Engineers help optimize the systems, allowing data scientists and analysts to perform their tasks. A data engineer’s task is to ensure data is appropriately collected, stored and made available to its users for data analytics and other purposes. Data engineers should possess strong technical knowledge for the creation and integration of API (Application Program Interface) and help in the maintenance of the data infrastructure.
In the following table, you can find the skill set required for these three roles in Data Science.
| Data Engineer | Data Analyst | Data Scientist |
| SQL | Analytics | R/Python coding |
| Data warehousing | Data warehousing | SQL |
| Hadoop | SQL | ML algorithms |
| Data Architecture | Statistical skills | Data Mining |
| Data Visualisation & reporting | Data Visualisation & reporting | Data optimisation and decision making skills |
Data Science has grown rapidly in recent years due to its wide applicability in various sectors and helps in strategic decision making for organizations.
Anyone can achieve great heights in Data Science with the appropriate skillset. If you wish to acquire skills in the various roles associated with Data Science, you can enroll in the Data Science Course in Chennai, provided by FITA Academy.
What are the job opportunities on course completion?
There are ample job opportunities for our students on course completion. Students are trained in higher-level languages like R/Python, and SQL, by professional trainers with hands-on experience in the field. With the skills acquired here, you can land your dream job in Data Science. Below we have listed a few of the roles which are in huge demand.
- Data Scientist
- Data Engineer
- Data Analyst
- Business Analyst
- Product Analyst
- Business Intelligence Analyst
FITA Academy ensures all of its students enrolled in the Data Science Training in Chennai are ready for all the above mentioned job roles.
What is the hiring process of a data scientist?
The hiring process for the role of data scientist differs based on companies.
Most of the startups will have an aptitude test comprising probability, statistics, logical reasoning, etc. Programming tests will be conducted to check your skills in Python or R or SQL. On clearing the test, there will be a final interview by the HR or Technical team.
While in MNCs, there will be an aptitude test as the first round, followed by an interview with a senior data scientist or person in any designation equivalent to it. Here the technical knowledge of the candidate is gauged and if the candidate is technically eligible, there might be a technical test to check the ability and expertise of the candidate in advanced tools utilized by a data scientist. In some companies, the candidate’s way of thinking and problem-solving approaches are also evaluated before hiring.
FITA Academy helps students enrolled in the Data Science course in Chennai getting used to the interview process. 100% placement training is provided by FITA Academy at the end of the course.
Here are Some of the job roles after Completing the Data Science Course in Chennai at FITA Academy.
Click Here: To explore more about the frequently asked Data Science Interview Questions and Answers for both Freshers and Experienced.
Student's Success Story of Data Science Course in Chennai
Preethi krishnan
It was a good experience to learn Data science. Here a practical oriented approach teaching was provided. The trainer was very friendly and taught me all the topics in detail.All the doubts were cleared immediately. The training infrastructure was very good. Many practical example were given.
Sushmita
FITA Academy is a good place to get Data Science Training under experts from the Data Science domain. The flexibly scheduled timing was more convenient for me to attend classes without any distractions. In the practical sessions, they offered training with hands-on projects which was more helpful for me to enhance my knowledge technically. Thanks to FITA Academy and the trainer.
Thenmozhi raj
I have done data science course here. Very friendly staff and wonderful atmosphere. Every session was perfect with the best explanation. Perfect place to learn this course.
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Frequently Asked Question (FAQ) about Data Science Course in Chennai
- This FITA Academy Data Science Course is designed and Trained by Data Science experts with 10+ years of experience as Data Analyst, Data Scientist, Data Engineer etc.
- A blend of hand-on practical sessions with real world examples is provided.
- Affordable fees, keeping students and IT working professionals in mind.
- Course timings designed to suit working professionals and students.
- Placement training provided at the end of the course.
Yes, You can Enroll in any of our branches in Velachery, Anna Nagar, T.Nagar or Tambaram and Thoraipakkam OMR. In every FITA Academy branch in Chennai, the data science syllabus and learning methodology are uniformly standardized.
- FITA Academy was started in 2012 by a group of experienced IT professionals with a vision to provide high quality IT training at affordable costs.
- As of now, FITA Academy has trained over 50000 students nationwide.
General Q & A about Data Science Course in Chennai
Which is best place to study a data science in Chennai?
Is data science costly?
Is data science a good career?
Can I learn data science in 1 year?
Is data science job in demand?
Does data science need coding?
Is C++ required for data science?
Which language is best for data science?
- Python
- SQL
- R
- Julia
- JavaScript
- Scala
- Java
- Go
Is data science a lot of math?
Is data science a safe career?
Can I learn Data Science in 3 months?
Who is Eligible for a Data Science Course?
Is Data Science a Difficult Course?
Can I Pursue Data Science if I’m Weak in Math?
How to Become a Data Scientist
- Proficiency in programming languages like Python or R.
- Strong understanding of statistics and mathematics.
- Skills in data manipulation and analysis using tools such as SQL and Pandas.
- Familiarity with machine learning algorithms and techniques.
Is Data Science a High-Paying Career?
Does Data Science Have a Promising Future?
Does Google Hire Data Scientists?
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