Data Science Training in Chennai
Upgrade your Data Science Skillset with our Data Analyst courses in Chennai!
Trained 15000+ Students | Course duration: 40 hours | Real-time Project Execution | Certification exam after course completion | Basic to advanced level learning |
Key Features
Course Duration : 8 Weeks
Live Projects : 1
Online Live Training
EMI Option Available
Certification & Job Assistance
24 x 7 Lifetime Support
Our Industry Expert Trainer
We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios.
The Global Certified Trainers are Excellent in knowledge and highly professionals.
The Trainers follow the Project-Based Learning Method in the Interactive sessions.
Overview of Data Science Training Course in Chennai
Our institution is considered the best data science training institute in Chennai. The importance of a data scientist who knows how to mock valuable intelligence from tons of data is increasing as many businesses open up their chances to data science. Additionally, data processing and analysis have enormous value, predicting an increase in data scientists’
demand. The 3RI’s Data Science Course in Chennai aims to provide candidates with the essential knowledge and abilities they need to succeed in the field, including data analytics, machine learning algorithms, data modelling, business analytics, k-means segmentation, and R programming. The Data Science Training in Chennai has been configured to suit the various industry needs. Those interested in advancing their careers in the field can select from any of the following tracks.
Data Science Course features
- Live Sessions
- Mocks, Assignments, & Tests
- Job Assistance
- 24/7 Lifetime Technical Support
- 10+ years of experience Proficient
- Real-time project experience
- Flexible Timings
Prerequisites
Basic knowledge of Python programming language, SQL, and files (MS Excel, CSV, etc.) with knowledge about algebra and geometry.
Course Duration
40 hours, i.e., 8-9 weeks approx.
Who all can apply for this course?
- Career switch Developers
- Candidates willing to start their career in Data Science or data analytics field
- Machine Learning or Hadoop background developers
- Data Analysts
- Business Analysts
Data science is a broad area that draws information and conclusions from both organized and unorganized information using scientific methodologies, procedures, techniques, and systems. Information extraction, deep learning, computer vision, big data, etc., are all integral parts of data science.
You can gain broad exposure to essential ideas and technologies from Python and R to machine learning and more with 3RI Technologies’ online data science courses. With our professors and training specialists guiding you throughout the course, practical labs and project work bring these concepts to life. You will become an expert in every system and tool data science professionals use. This course includes training in Python,
Statistics, Apache Spark & Scala, Tableau, and all the other renowned and related technologies.
You may learn programming languages, machine learning algorithms, and more with 3RI’s online Data Science curriculum. Utilize your chance to get proficient in the newest technology right away. Today, develop your skills as a Data Scientist. After this course, you will receive certificates from 3RI Technologies for the learning path’s Data Science courses. Your
skill set as a specialist in data science and all of its facets will undoubtedly grow due to these qualifications. Additionally, this certificate will give you an advantage when applying for exceptional opportunities. You will also get reasonable data science course fees in Chennai.
Obtaining a data science qualification can give you an advantage in today’s competitive job market. You will learn the principles of data science in this course, equipping you with the knowledge and abilities necessary to analyze and interpret data. The following are some advantages of enrolling in our data science training in Chennai:
● Career Opportunities: Many businesses are now recruiting data scientists, which is excellent news for people who wish to pursue a career in this field.
● Flexible Hours: You can set your hours based on the candidates’ availability. Your convenience will be taken into account when doing the training.
● Real-time Project Work: To help students gain practical experience, we will assign real-time projects throughout the course.
● Comprehensive Knowledge: This data science course in Chennai has been created based on industry standards and covers every area of data science.
● Best Sector Experts: All of our instructors are working professionals with a minimum of five years of experience, so they know the demands. They stay current on the newest fashions and technological advancements.
● Placement Assistance: We never compromise on our commitment to delivering high-quality education. Therefore, you will receive placement support after completing the course.
● Gain Practical Experience: Our curriculum is set up to give you the best exposure to the subjects covered in each module.
● Fast Track Learning Technique: You may easily understand concepts with the help of our learning methodology.
● Are you new to data science? There are no prerequisites. We
addressed subjects such as fundamental data analysis with the R programming language. If you have no programming experience, you are welcome to join us.
● Assurance of Success: Your success is our success. We care about your future by ensuring that you accomplish exceptional outcomes.
There are numerous advantages to pursuing our Data Science Course in Chennai. Students will learn the skills and knowledge required to enter the data analytics sector, boost their productivity, and develop a stronger foundation in statistics, to mention a few benefits. We hope that this training will assist you in achieving your career objectives. Data science course fees in Chennai cover all these benefits.
However, supply is not keeping up with the demand for data scientists. As a result, now is the ideal time to become a data scientist. Employers are increasingly interested in hiring data scientists. Huge organizations need data scientists to turn the massive volumes of data from social
media and e-commerce sites into action. Most businesses see data scientists as the best way to embrace AI technologies.
The best news is that, in addition to all major corporations and digital natives, smaller businesses are eager to engage in data mining operations. With all of this comes the estimate of a 30% growth in the number of data science positions over the last year. It is, undoubtedly, the most significant moment to hone your data science skills.
Data Science Implementation As we all know, data science is a broad field that employs a variety of tools for various activities. Data Science consists of four major processes: data integration and cleansing, data warehousing, data analytics, and data visualization. Let us now look at the primary technologies utilized to implement Data Science for these various activities.
● Integration and cleansing of data The first stage of the Data Science lifecycle is data acquisition. There are several methods for gathering data. The major challenge is that the data obtained is valuable and dependable for the business. Furthermore, the collected data is not usually structured. It can also be semi-structured or unstructured.
Moreover, the amount of data gathered will be enormous. Numerous standard ETL solutions can help Data Scientists with their tasks. The popular ETL Tools and their features are listed below.
● Data Collection and Cleaning Talend, IBM Data Camp, and OnBase are the tools used here.
Skills Required
- No Prerequisites for Data Science certification training
- Basic knowledge of SQL is advantageous
Data Science Course Syllabus
Decade Years Legacy of Excellence | Multiple Cities | Manifold Campuses | Global Career Offers
- Fundamentals of Data Science and Machine Learning
- Introduction to Data Science
- Need of Data Science
- BigData and Data Science’
- Data Science and machine learning
- Data Science Life Cycle
- Data Science Platform
- Data Science Use Cases
- Skill Required for Data Science
- Mathematics For Data Science
- Linear Algebra
- Vectors
- Matrices
- Optimization
- Theory Of optimization
- Gradients Descent
- Introduction to Statistics
- Descriptive vs. Inferential Statistics
- Types of data
- Measures of central tendency and dispersion
- Hypothesis & inferences
- Hypothesis Testing
- Confidence Interval
- Central Limit Theorem
- Probability and Probability Distributions
- Probability Theory
- Conditional Probability
- Data Distribution
- Distribution Functions
- Normal Distribution
- Binomial Distribution
- An Introduction to Python
- Why Python , its Unique Feature and where to use it?
- Python environment Setup/shell
- Installing Anaconda
- Understanding the Jupyter notebook
- Python Identifiers, Keywords
- Discussion about installed module s and packages
- Conditional Statement ,Loops and File Handling
- Python Data Types and Variable
- Condition and Loops in Python
- Decorators
- Python Modules & Packages
- Python Files and Directories manipulations
- Use various files and directory functions for OS operations
- Python Core Objects and Functions
- Built in modules (Library Functions)
- Numeric and Math’s Module
- String/List/Dictionaries/Tuple
- Complex Data structures in Python
- Python built in function
- Python user defined functions
4. Introduction to NumPy
- Array Operations
- Arrays Functions
- Array Mathematics
- Array Manipulation
- Array I/O
- Importing Files with Numpy
5. Data Manipulation with Pandas
- Data Frames
- I/O
- Selection in DFs
- Retrieving in DFs
- Applying Functions
- Reshaping the DFs – Pivot
- Combining DFs
Merge
Join - Data Alignment
6. SciPy
- Matrices Operations
- Create matrices
Inverse, Transpose, Trace, Norms , Rank etc - Matrices Decomposition
Eigen Values & vectors
SVDs
7. MatPlotLib & Seaborn
- Basics of Plotting
- Plots Generation
- Customization
- Store Plots
8. SciKit Learn
- Basics
- Data Loading
- Train/Test Data generation
- Preprocessing
- Generate Model
- Evaluate Models
9. Descriptive Statistics
. Data understanding
- Observations, variables, and data matrices
- Types of variables
- Measures of Central Tendency
- Arithmetic Mean / Average
- Merits & Demerits of Arithmetic Mean and Mode
- Merits & Demerits of Mode and Median
- Merits & Demerits of Median Variance
10. Probability Basics
- Notation and Terminology
- Unions and Intersections
- Conditional Probability and Independence
11. Probability Distributions
- Random Variable
- Probability Distributions
- Probability Mass Function
- Parameters vs. Statistics
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Standard Normal Distribution
- Central Limit Theorem
- Cumulative Distribution function
12. Tests of Hypothesis
- Large Sample Test
- Small Sample Test
- One Sample: Testing Population Mean
- Hypothesis in One Sample z-test
- Two Sample: Testing Population Mean
- One Sample t-test – Two Sample t-test
- Paired t-test
- Hypothesis in Paired Samples t-test
- Chi-Square test
13. Data Analysis
- Case study- Netflix
- Deep analysis on Netflix data
- Exploratory Data Analysis
- Data Exploration
- Missing Value handling
- Outliers Handling
- Feature Engineering
- Feature Selection
- Importance of Feature Selection in Machine Learning
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Machine Learning: Supervised Algorithms Classification
- Introduction to Machine Learning
- Logistic Regression
- Naïve Bays Algorithm
- K-Nearest Neighbor Algorithm
- Decision Tress
- SingleTree
- Random Forest
- Support Vector Machines
- Model Ensemble
- Model Evaluation and performance
- K-Fold Cross Validation
- ROC, AUC etc…
- Hyper parameter tuning
- Regression
- classification
- Machine Learning: Regression
- Simple Linear Regression
- Multiple Linear Regression
- Decision Tree and Random Forest Regression
- Machine Learning: Unsupervised Learning Algorithms
- Similarity Measures
- Cluster Analysis and Similarity Measures
- Ensemble algorithms
- Bagging
- Boosting
- Voting
- Stacking
- K-means Clustering
- Hierarchical Clustering
- Principal Components Analysis
- Association Rules Mining & Market Basket Analysis
- Machine Learning end to end Project blueprint
- Case study on real data after each model.
- Regression predictive modeling – E-commerce
- Classification predictive modeling – Binary Classification
- Case study on Binary Classification – Bank Marketing
- Case study on Sales Forecasting and market analysis
- Widespread coverage for each Topic
- Various Approaches to Solve Data Science Problem
- Pros and Cons of Various Algorithms and approaches
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Who can apply for the course?
- Aspiring Data Scientists who are interested in switching careers.
- Graduate/post-graduate students wishing to pursue their careers in Data Analytics/Data Science.
- Professionals who work with big data.
- Professionals from non-IT bkg, and want to establish in IT.
- Candidate who would like to restart their career after a gap.
- Machine learning is a topic of interest to professionals.
- Business analysts and those who work with data
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Industry Projects
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- Project Implementation with Real-Time Scenario.
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