1st Oct 2023-(4th Cohort)
8 Months (240 Hrs.)
Why to Join This Course
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Curriculum for Data Science Bootcamp
Basics of Python and Data Structures
- Python Basic Syntaxes and Data Structures
- Python Programming Constructs and Functions
- Developing Logic in Programming
- Data Structures
- I/O, Error Handling and Best Practices
- Functional Programming (Filter, map, reduce, lambda)
- NumPy and Pandas-Walkthrough of Major Syntaxes
- Manipulating Databases through Pandas & Built-in Functions
- API creation and Management using Python
- Data visualization using python
Understand about how to retrieve and store data in various commonly used data sources
- Understand about how to retrieve and store data
Interpretation and deduction level understanding of key mathematical concepts required in ML
- Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distributions (Binomial, Poisson, Normal), p-value, Bayes’ theorem
- Permutation Combination
- Basics of integration & differentiation
- Laplace Transform
- Fourier Transformation
- Solution of Matrix by Gauss’s Elimination Method
- Defining Vector Space and Linear Combination
- Brief on Eigen Values and Eigen Vectors
Introduction to Data Science
- Data Science Life Cycle Management
- Type of Learning- Supervised, Unsupervised, Semi-supervised, Reinforcement
- Ability to perform EDA using SQI
- Ability to perform EDA using python function
- Ability to perform EDA using excel
- Ability to draw hypothesis by analyzing EDA outcomes
- Ability to create time-based features
- Ability to create relationship-based features
- Ability to create frequency-based features
- Ability to create frequency-based algorithms
- Ability to identify important features
- Introduction to linear regression
- Introduction to unsupervised learning K-means and Hierarchical
- Introduction to unsupervised learning ability to choose optimum number of clusters and key metrics
- Introduction to forecasting- ARIMA
- Introduction to tree based algo- Decision tree
- Model accuracy calculation
Data science 2
Advance regression Techniques
- Nonlinear regression
- Elastic net regression
- Rasso and Ridge regression
Advance Tree based Algorithm
- Bagging and Boosting
- Random forest
- Types of boosting trees -GBT, Cat boosting/Ada Boosting
- Optimization of Tree based algorithm
Optimization of SVM
Introduction to Deep Learning
- Introduction to ANN
- Auto Encoders
Advance forecasting Techniques
- Introduction to reinforcement learning
- Introduction to its architecture
- Working with its dashboard
- Implementation data blending and aggregation
- Data visualization and real time analytics
- Generated fields and special fields
- Connecting python scripts in Tableau
- Connections for organizing data
- Tableau graphs, report, and calculations
- Data Storytelling fundamentals and frameworks
Stage 7 Specialization
- Common -MLOPS
Upon completion of this course, students will be able to:
- Perform data manipulation, transformation, and analysis using Python and popular data libraries like NumPy and Pandas.
- Retrieve and store data from various data sources, including relational databases and NoSQL databases.
- Apply mathematical concepts such as probability, statistics, and linear algebra to solve data science problems.
- Conduct exploratory data analysis (EDA) and create actionable insights from data.
- Implement regression, decision trees, and other machine learning algorithms for predictive modeling.
- Utilize deep learning techniques, including artificial neural networks and autoencoders, for advanced data analysis.
- Create interactive and informative data visualizations using Tableau for effective data communication.
- Specialize in one of the emerging areas in data science, such as MLOPS, NLP, Computer Vision (CV), or Geospatial data analysis.