Data Science Course in Chennai
Data Science Course in Chennai
Data Science Course = Top career choice
Data science is an interdisciplinary field that combines programming, statistics, and business intelligence with fields like machine learning, artificial intelligence, and business analytics to get valuable information from large amounts of data.
Data science is a new and popular field with a lot of opportunities.
Data scientists are in high demand because their skills can be used in many different fields, such as Healthcare, Finance, Retail, Education, and many more. Since 2012, the demand for data scientists has grown by 31% every year. Their average salary ranges from 6 to 10 lacs per year in India, depending on the location and size of the company.
If you have good skills in Data Science, you can get jobs like Data Analyst, Business Intelligence Analyst, Data Visualizer, etc., to start with.
Data science is one of the top five careers that young people worldwide want to go into, and it's only going to get stronger in the years to come.
1. Trainers from Industry
All our trainers have gained real time experience working in reputed companies & have immense knowledge in the Data Science field.
2. Small batch size
We take only 6 students per batch so that we can pay more attention to each one.
3. Frills-free syllabus
We only teach what a student needs to know to enter the Data Science field, which is different from most institutes that try to attract students with fancy terminologies and needless topics.
4. Projects based training (SOAP)
We give our students a lot of tasks and assignments through a system we call SOAP (Student Output Assessment Plan), and we also give them helpful feedback.
5. Live Interactive sessions
Since our batch sizes are small, it would truly be an interactive session where we encourage our students to ask any number of doubts during the class.
6. Recorded sessions
All sessions would be recorded on video and given to the students so they could watch and learn from them later.
7. Placement support
We have a dedicated placement team that supports all our students in their placement journey, starting with Resume building until securing a good job.
Data Science Courses Syllabus
Module 1 : Introduction to Data Science
- What is Data Science
- What is Machine Learning
- What is Deep Learning
- What is AI?
- Data Analytics and its types
- Why python?
Module 2 : Python (Basics to advanced)
- Installation and google colab setup
- Understanding various python notebooks like jupyter,spider.
- Variables and data types: numbers, Boolean and strings
- Operators
- Conditional statements
- Functions
- Sequences
- Files and Classes
- Object oriented programming
- Inheritance
Module 3 : Python Packages
- Numpy
- Pandas
- Matplotlib
- Seaborn
Module 4 : Statistics
- Types of statistics
- Descriptive statistics
- Types of data
- Population and sample
- Mean, median, mode
- Regression
- Variability
- R-squared
- Correlation
- Covariance
- Distribution
- Normal distribution
- Standard normal distribution
- Central limit theorem
- Standard error
- Confidence intervals
- Z-score
- Margin of errors
- Hypothesis Testing
Module 5 : Linear Algebra Basics
- Vectors
- Matrix
Module 6 : Probability
- Basic probability
- Computing expected values
- Events
- Combinatorics
- Factorials
- Bayesian inference
- Sets and Events
- Probability distributions
- Discrete distributions
- Applications of probability in statistics
- Applications of probability in Data Science
Module 7 : Data Preprocessing
- Handling missing values
- Encoding categorical data
- Split the dataset
- Feature scaling
Module 8 : Exploratory Data Analysis
- Feature Engineering
- Data Visualization - PowerBi (Basic to Advanced)
- Different chart types
Module 9 : Machine Learning
- Introduction to ML
- Types of AI
- Stages of ML projects
- Types of ML algorithms
Module 10 : Regression
- Simple linear regression
- Multi linear regression
- Model Evaluation
- Project : Kaggle bike demand prediction
Module 11 : Classification
- Logistics regression
- Bagging and Boosting
- SVM
- KNN
- DECISION TREES
- RANDOM FOREST
- XG BOOST CLASSIFIER
- NAÏVE BAYES
- Model Evaluation
- Project : open Kaggle Competition Project
Module 12 : Clustering and Time Series Analysis
- K means cluster
- Hierarchical clustering
- model evaluation
- parameter tunning
- model visualization
- Introduction to Time Series Data
- Time Series Forecasting Methods
Module 13 : Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing
- Bag of Words Model
- TF-IDF Model
- Sentiment Analysis
- NLP Applications (Chatbots, Text Summarization, Language Translation)
Module 14 : Model Tuning
- Hyperparameter Optimization
- Grid Search
- Random Grid Search
- Bayesian Optimization
Module 15 : Recommendation System
- Content-based filtering
- Collaborative based filtering
- Market basket Analysis
Module 16 : Databases
- SQL
Module 17 : Deep Learning
- Tensor flow and Keras
- Deep learning frame work
- CNN and RNN
Module 17 : Flask
- Creating RestFul API with Flask
- Postman / ARC Chrome