- Python Programmming Language Concepts.
- Database Programming with SQL.
- Statistics for Data Science.
- Linear Algebra for Data Science.
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
- IT Professionals.
- Analytics Managers.
- Business Analysts.
- Banking and Finance Professionals.
- Introduction to Big Data, State of the practice in analytics.
- Current Analytical Architecture.
- Drivers of Big Data, Emerging Big Data Ecosystem.
- Big Data Analytics Project Life Cycle.
- Introduction to Machine Learning.
- Introduction to Python.
- Introduction to Lists, Ranges & Tuples in Python.
- Python Dictionaries and Sets.
- Input and Output in Python.
- Python built in function.
- Python Object Oriented.
- Python Exceptions Handling.
- Python Regular Expressions.
- Python Multithreaded Programming.
- Using Databases in Python.
- Python for Data Analysis Numpy.
- Introduction.
- Entities and Attributes.
- Relationship Fundamentals.
- SELECT and WHERE.
- WHERE, ORDER BY, GROUP BY, HAVING and Intro to Functions.
- Single Row Functions.
- Joins.
- Data Manipulation Language (DML).
- Data Definition Language (DDL).
- Constraints.
- Views.
- Datatypes and its measures.
- Random Variables and its applications.
- Introduction to Probability with examples.
- Sampling Techniques – Why and How.
- Measures of Central Tendency- Mean, Median, Mode.
- Measures of Dispersion- Variance, Standard Deviation, Range.
- Measures of Skewness & Kurtosis.
- Normality tests for dataset.
- Basic Graph Representations- Bar Chart, Histogram, Box Plot, Scatterplot.
- Probability Distributions.
- Continuous Probability Distribution.
- Discrete Probability Distribution.
- Building Normal Q-Q Plot and its Interpretation.
- Central Limit Theorem for sampling variations.
- Confidence Interval – Computation and analysis.
- Data Cleansing (Dealing with Missing Data, Outlier Detection).
- Feature Engineering (Label Encoding, One-Hot Encoding).
- Data Transformation, including merging, ordering, aggregation.
- Sampling (Balanced, Stratified).
- Data Partitioning (Create Training + Validation + Test Data Set).
- Transformations (Normalization, Standardization, Scaling, Pivoting).
- Binning (Count-Based, Handling Of Missing Values as its own Group).
- Data Replacement (Cutting, Splitting, Merging.
- Weighting And Selection (Attribute Weighting, Automatic Optimization).
- Imputation (Replacement of Missing Observations with Statistical Algorithms).
- Formulating a hypothesis statement (NULL and ALTERNATE).
- Type-I and Type-II Errors, P-Value, Level of Significance.
- Parametric Tests: One Sample/Two Samples T Test, One Sample Z Test, Paired T Test, One-Way ANOVA, Chi-Squared Test.
- Non-Parametric Tests: One Sample Sign Test, Mann-Whitney Test, Kruskal-Wallis Test.
- Representation of problems in Linear Algebra.
- Matrix..
- Solving the problem (Row Echelon form & Inverse of a Matrix).
- Eigenvalues and Eigenvectors.
- Singular Value Decomposition of a Matrix.
- Correlation Analysis, Correlation Coefficient.
- Introduction of Regression, Principles of regression.
- Simple Linear Regression Analysis.
- Splitting of Dataset into Train, Validation and Test data.
- Understanding Over fitting (Variance) vs Under fitting (Bias).
- Generalization Error and Regularization Techniques.
- Multiple Linear Regression Model.
- Model Adequacy Checking.
- Transformation and Weighting to Correct Model Inadequacies.
- Diagnostic for Leverage and Influence.
- Generalized and Weighted Least Squares Estimation.
- Indicator Variables.
- Multicollinearity, Heteroskedasticity, Autocorrelation.
- Polynomial Regression Models.
- Poisson Regression Models.
- Variable Selection and Model Building.
- Two-Class Classification.
- Multiclass Classification.
- Anomaly Detection.
- Partitioning Clustering.
- Hierarchical Clustering.
- Clustering Validation and Evaluation with K-Means Clustering.
- DBSCAN: Density-based Clustering.
- Dimensionality Reduction with Principal Component Analysis(PCA).
- Association Rule Learning and Recommendation.
- Model Performance Assessment.
- Hyper Parameter Optimization/Tuning.
- Cross-Fold Validation Techniques.
- Ensemble Methods in Machine Learning.
- Data Visualization with Matplotlib, Seaborn, Tableau.
- Relational Plots.
- Categorical Plots.
- Distribution Plots.
- Regression Plots.
- Matrix Plots.
- Tableau Products & Usage.
- Natural Language Processing - What is it used for?
- NLTK Exploration.
- Bag of Words.
- TF- IDF Vectorizer.
- Co-occurrence matrix.
- Text Similarity/Clustering.
- Latent Semantic Analysis (LSA).
- Topic Modelling.
- Latent Dirichlet Allocation (LDA).
- Text Classification - Sentiment Analysis.
- Recommender Systems - Collaborative Filtering.
- Introduction to Time Series Data.
- Correlation and Autocorrelation.
- Components of Time Series.
- Visualization Principles - Scatter Plot, Time Plot and Lag Plot.
- Auto-Correlation Function (ACF)/ Correlogram.
- Naive Forecast Methods.
- Errors in Forecast and Its Metrics.
- Model Based Approaches.
- Auto Regression (AR), Moving Average (MA).
- Autoregressive Moving Average (ARMA).
- Autoregressive Integrated Moving Average (ARIMA).
- Additive Seasonality.
- Multiplicative Seasonality.
- Random Walk.
- Smoothing Techniques.
- De-Seasoning and De-Trending.
