Introduction to Data Science
- Introduction ToDataScience
- Real Time UseCasesOfDataScience
- Who is a DataScientist?
- Github Tutorial
- Skillsets needed for a data scientist
- 6 Steps to take in 3 Months for a complete transformation to DataSciencefrom any other domain
- Machine Learning-Giving computers the ability to learn from data
- Supervised vs Unsupervised
- DeepLearning vs Machine Learning
- Link to get Free Data to Practice?
- Some Great self LearningDataScienceResources(Books, Tutorials,Videos, Papers)
- Software Installation
Python Programming
- Introduction To Python
- “Hello Python Program” in IDLE
- Jupyter Notebook Tutorial
- Spyder Tutorial
- Introduction to Python
- Variable,Operators,DataTypes
- If Else, For and While Loops
- Functions
- Lambda Expression
- Filter, Map, Reduce
- Taking input from the keyboard
- HANDS ON-
- INTERVIEW QUESTION DISCUSSION
Python Advanced Topics
- NumPy
- Create Arrays
- Array Item Selection and Indexing
- Array Mathematics
- Array Operation
- HANDS ON
- Pandas
- Introduction to Pandas
- Series
- Series indexing and Selection
- Series Operation
- Introduction to Pandas
- Data Frames
- Data Collection from csv,json,html,excel
- Data Merging,Concatenation,join
- Group By and Aggregate Function
- Order By
- Missing Value Treatment
- Outlier Detection and Removal
- Pandas builtin Data Visualisation
- HANDS ON
- INTERVIEW QUESTION DISCUSSION
Visualisation-matplotlib, seaborn
- Line Plots
- Scatter Plots
- Pair Plots
- Histograms
- Heat Maps
- Bar Plots
- Count Plots
- Factor Plots
- Box Plots
- Violin Plots
- Swarm Plots
- Strip Plots
- Pandas BuiltinVisualisation Library
- HANDS-ON
- INTERVIEW QUESTION DISCUSSION
Statistics
- Descriptive vs Inferential Statistics
- Mean,Median,Mode,Variance,Std. dev
- Central Limit Theorm
- Co-Variance
- Pearson’s Product Moment Correlation
- R - Square
- Adjusted R-Square
- Spearman’s. Rank order Coefficient
- Sample vs Population
- Standardizing Data(Z-score)
- Hypothesis Testing
- Normal Distribution
- Bias Variance Tradeoff
- Skewness
- P Value
- Z-test vs T-test
- The F distribution
- The chi-Square test of Independence
- Type-1 and Type-2 errors
- Annova
- HANDS ON
- INTERVIEW QUESTION DISCUSSION
Introduction to Machine Learning
- Introduction to Machine Leaning
- Machine Learning Usecases
- Supervised vs Unsupervised vs Semi-Supervised
- Machine Learning process Workflow
- Training a model
- Validating results
- Overfitting vs Underfitting
- Ordinal vs Nominal data
- Structured vs unstructured vs semi-structured data
- Intro to sci-kit learn
- HANDS-ON
Supervised
- Regression:
- Regression Vs Classification
- Linear regression
- Multivariate regression
- Polynomial regression
- Multi-Colinearity,
- Autocorrelation
- Heteroscedasticity
Hands-On
- Classification:
- KNN
- Svm
- Decision Tree
- Random Forest
- Performance tuning of Random Forest
- Naive Bayes
- Overfitting Vs Underfitting
- Hands-On
Validation
- Classification Report
- Confusion Report
- ROC
- RMSE
- MSE
- Cross validation
- Hands On