Become an expert in
Data Science and Machine Learning

Introduction to NumPy, installing NumPy, N-Dimensional array, array creation routines (array of ones & zeros, from existing data, using numerical ranges, etc.), Arithmetic Operators, Single Dimensional Arrays, Multi-Dimensional Arrays, matrix product, NumPy functions (universal functions, aggregate functions, & logic functions), Indexing of NumPy arrays, fancy indexing, slicing of NumPy arrays, Iterating in a NumPy array, array manipulation, changing array shape, transpose like operations, joining arrays (concatenate, stack, column_stack, hstack & vstack) , splitting arrays (split, hsplit & vsplit), file handling in NumPy (loading and saving data in binary file, loading and saving data in text file), merging NumPy arrays, NumPy case study – creating NumPy array, indexing & slicing, statistical calculation using NumPy, mathematical operation using NumPy, sorting a NumPy array.

Introduction to pandas, functionality of pandas, significance of pandas, installing pandas, pandas data structure, pandas series, pandas dataframe, importing data or reading data from CSV file, Excel file & JSON file, exporting data or writing data to CSV file & Excel file, Essential functionality of series & dataframe, selecting columns, selecting rows, adding columns, removing columns, removing rows, updating cells, filtering a dataframe (using a single value, & using a list), concatenate rows & columns, append rows, & merge data, join operation (left, right, outer, inner), data cleaning, handling missing data using pandas, inspecting and removing duplicates, replacing values, groups & aggregation, grouping data using pandas, case studies.

Introduction to Data Visualization, Libraries and tools for data visualization, Introduction to Matplotlib library, types of plots & charts (line plot, bar chart, horizontal bar chart, stacked bar chart, grouped bar chart, histogram, etc.), introduction to Seaborn library, benefits of Seaborn library, types of plots & charts (line plot, bar plot, scatter plot, histogram, box plot, pie chart, heat map, etc.), plotting different types of plots & charts, customizing visualizations using Matplotlib (style, labels & ticks, colors, linestyle, markers, & legend), saving plots, subplots, grid, case studies.

Introduction to Web Scrapping, use cases, need of web scrapping in data science, web scrapping process flow, popular tools for web scrapping, requests, Introduction to Beautiful soup library, installing beautiful soup, creating soup, types of objects, inspecting a web page, web scrapping demonstration using beautiful soup.

Introduction to Data types, categorical data, numerical data, data I/O. Introduction to statistics, statistical analysis divisions, population & sample. Introduction to measures of central tendency (mean, median & mode), calculate measure of central tendency using python. Introduction to measures of Dispersion (range, interquartile range, variance, & standard deviation), skewness & kurtosis, calculate measures of dispersion using python. Introduction to measures of position (percentiles, quartiles, & standard scores), calculate measures of position using python. Introduction to Exploratory Data Analysis (EDA), significance of EDA, data analysis techniques, EDA classification, Basics of Univariate Non-Graphical EDA, existence of outliers, detecting and removing of outliers, measures of shape, data visualization using statistical graphs – Pie charts, , Bar graphs, Histograms, Line graphs, Box plots, Dot plots, Basics of Multivariate EDA, analyzing multivariate Non-Graphical EDA, perform cross tabulation on data, covariance & correlation, association, causation, correlation matrix, Analyzing multivariate graphical EDA, visualize data using scatter plot, visualize data using heat maps.

Introduction to Probability, Need for probability theory, common concepts of probability, probability demonstration using python, union & intersection of events, classify joint and disjoint events, dependency of events, calculating probability of events, conditional probability. Introduction of Bayes’ theorem, need for Bayes’ theorem, calculating probability through Bayes’ theorem, application of Bayes’ theorem in data science, introduction to expected values, need for calculating expected values, problem demonstration. Introduction to Probability Distributions (PD), effect of standard deviation on distribution, standardization, Z-score, Skewness & kurtosis in Distribution graph, problem demonstration, types of probability distributions, t-distribution, degree of freedom (mean), problem demonstration. Introduction to sampling distribution, need for sampling distribution, case studies, standard error, standard error of mean, problem demonstration, central limit theorem, case study and exercises. Introduction to Inferential statistics, forms of statistical inference, Estimation, Bias of an Estimator, point estimation and interval estimation, Mean Square Error (MSE), Bias & variance using Bull’s Eye diagram, confidence interval, Margin of Error, confidence interval estimation, problem demonstration. Introduction to Hypothesis, statistical hypothesis, hypothesis testing, decision errors in hypothesis testing, decision rules, introduction to statistical test, P-value & critical value, calculating P-value from Z-scores, thumb rule in hypothesis testing, case study, One tailed test, two tailed test, z-test, hypothesis test for single population mean (T-Test), independent two sample T-Test, performing T-Test, problem demonstration, basics of Chi-square test, types of chi-square test, problem demonstration, Examine National Health & Nutrition Examination Survey (NHANES) data – case study.

Introduction to Natural Language Processing, Use cases of NLP, need of processing textual data, Application of NLP, install natural Language Tool Kit (NLTK), download NLTK packages, NLTK exercises, Introduction to text preprocessing & tokenization, types of tokenizers (bigrams, trigrams & ngrams), creating tokens using NLTK, Part of Speech (POS) tagging, steps of POS tagging, stop words, removing stop words using NLTK, problem demonstration. Introduction to stemming, stemming using NLK, Porter & Lancaster stemmer, NonEnglish stemmer, introduction to Lemmatization, Lemmatization using NLTK, problem demonstration. Introduction to Named Entity Recognition (NER), NER using NLTK, Word Sense Disambiguation (WSD), WSD using NLTK, problem demonstration. Introduction to Feature Extraction, Bag-of-Words model, case study & exercises, Term Frequency – Inverse Document Frequency (TF-IDF), implement TF-IDF using Python, problem demonstration. Introduction to Sentiment Analysis, TextBlob for sentiment analysis, steps to perform sentiment analysis, sentiment analysis of twitter data using NLP, problem demonstration.

Introduction to Machine Learning, AI vs Machine Learning vs Deep Learning, machine learning applications, types of machine learning, Reward or Penalty (RL), Data preprocessing, data preprocessing techniques, imputing missing values, handling categorical values, scaling the data, StandardScaler, MinMaxScaler, RobustScaler, Normalization, feature selection, problem demonstration. Introduction to data splitting, training data, testing data, supervised learning, types of supervised learning, regression & its types, Linear regression & its types, problem demonstration, r-square, variance inflation factor (VIF), problem demonstration. Introduction to gradient descent, error minimization, regularization and its types, ridge regression, lasso regression, elastic net regression, regression case study, introduction to classification algorithm, problem demonstration. Introduction to logistic regression, logistic regression : functions, odds, activation function, cost function, update function, problem demonstration. Introduction to decision tree, decision tree terminology, CART algorithm, problem demonstration, introduction to impurity, gini impurity, building decision tree, selecting best feature to split, information gain, ID3 algorithm, entropy, problem demonstration. Introduction to Random Forest, Ensemble methods : Bagging & Boosting, Creating random forest, introduction to performance measurements, SMOTE, precision, recall & FI score, problem demonstration. Introduction to Naïve Bayes, conditional probability & Bayes theorem, Naïve bayes calculation, naïve bayes in Scikit module, gaussian naïve bayes, Bernoulli naïve bayes, multinominal naïve bayes, problem demonstration. Introduction to K Nearest Neighbor, KNN working, distance metric, minkowski distance, significance of K in KNN algorithm, problem demonstration. Introduction to Support Vector Machine, SVM terminologies, calculating hyperplane, soft margin classifier, nonlinear SVM, Kernel Trick, SVM Kernels, Gaussian RBF, Polynomial kernel, problem demonstration.

Introduction to Dimensionality, Curse of Dimensionality, Dimensionality Reduction, Techniques of Dimensionality Reduction, Introduction to Principal Component Analysis (PCA), Dimensionality Reduction with PCA, Working with Dimensional Data, Problem Demonstration. Introduction to Linear Discriminant Analysis (LDA), Working of LDA, LDA & PDA comparison, other techniques for dimensionality reduction, missing value ratio, low variance filter, random forest, high correlation filter, Problem Demonstration. Introduction to Unsupervised Learning, Process Flow & Example, Clustering, types of clustering (exclusive, overlapping & hierarchical), K-Means Clustering Algorithm, Elbow Method, Applying K-Means Algorithm on 2D plots, Problem Demonstration. Introduction to Fuzzy C-Means Clustering, Problem Demonstration, DBSCAN (Density Based Spatial Clustering of Application with Noise) clustering algorithm, Problem Demonstration. Introduction to Association Rule Mining, Parameters (Support, Confidence, Lift), Generating Association Rules, Apriori Algorithm, Market Based Analysis, Problem Demonstration. Introduction to Recommendation System, Cosine-Based Similarity, Coverage, Common types of Recommender System, User Based Collaborative Filtering (UBCF), Content Based Filtering (CBF), User Driven Content and Service, Recommending similar movie to the user. Introduction to Time Series Analysis, Time Series Components (Trend, Seasonality, Cyclical Patterns, & Irregularity), Forms of Data (Stationary Data & Nonstationary Data), methods to check for stationary of data, Augmented Dicky-Fuller (ADF) Test, converting nonstationary data to stationary data, AutoCorrelation Function (ACF) and Partial AutoCorrelation Function (PACF), Auto Regression Model, Moving Average Model, Autoregressive Moving Average (ARMA) Model, ARIMA Model, Problem Demonstration & Case Studies.

Introduction to Model Selection, Resampling Techniques for Model Selection, Resampling Measures, K-Fold Cross Validation, Introduction to Model Evaluation, Problem Demonstration. Model Evaluation Metrics for Regression, Model Evaluation Metrices for Classification, Test Statistics, Confusion Matrix, Calculating Confusion Matrix, Problem Demonstration. Introduction to ROC Curve, Understanding the operation of ROC, Plotting ROC Curve, AUC Curve Operation, Problem Demonstration, Introduction to Precision and Recall, F1 Score, Problem Demonstration. Introduction to Hyperparameter Tuning, Types of Hyperparameter Optimization, Manual Search, Grid Search, Random Search, perform Grid Search, Problem Demonstration. Introduction to Ensemble Learning, Ensemble Learning Methods (Bagging, Boosting & Stacking), Bagging stages, Bagging Workflow, Problem Demonstration, Bagging Vs Boosting, Boosting Algorithms, Adaptive Boosting (AdaBoost), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Problem Demonstration. Introduction to Model Optimization, Elements of Optimization, Linear Programming Basics, Linear Programming Applications, Problem Demonstration, formulating Optimization Problem, Stochastic Gradient Descent (SGD), Accelerated Gradient Methods, Second-Order Methods, Problem demonstration & Case Studies.

Introduction to deep learning, use cases, structure & functionality of human brain, functionality of a machine, Neural Network, Artificial Neural Network, biological vs artificial neuron. Introduction to Perceptron, Activation Function, sigmoid function, Tanh function, Rectified Liner Unit (ReLu) function, Softmax function, Multilayer Perceptron (MLP), Neural Network Evaluation, Improving Neural Network Performance, Gradient Descent to Cost Function. Introduction to Backpropagation, Learning Rate, Neural Network Learning, Exercises. Introduction to TensorFlow, basic components, building & running a graph, Eager Execution, Introduction to Keras, TensorFlow installation, building a neural network in TensorFlow, problem demonstration, Image classification using TensorFlow. Introduction to Convolutional Neural Network (CNN), Limitations of Multilayer Perceptron, CNN vs MLP, Working of Convolutional Layer, ReLu, Pooling Layer, Fully Connected Layer, Image Recognition, Rules of Image Recognition Process, Image classification using CNN, Libraries Required for Prediction, building a CNN model, Problem Demonstration. Introduction to Recurrent Neural Network (RNN), Issues with Feed Forward Network, Architecture of RNN (One to One, Many to One, One to Many, & Many to Many), Problem Demonstration, Training RNN, Long Short-Term Memory (LSTM) networks, Issues with RNN, LSTM Structure (Forget gate, Input gate, & Output gate), Problem Demonstration. Introduction to Reinforcement Learning (RL) , use cases and challenges, RL Process, Reward Hypothesis, RL Agent Components (Environment, Agent and Information State), RL Agent Taxonomy Types, Value Based RL, Policy Based RL, & Model Based RL. Case Studies & Exercises.

Introduction to Tableau, Tableau Products, VizQL language, Data Connections, Connect to data from file, server or database, Creating Bar Charts, Line Charts & Pie Charts. Introduction to Data Grouping (group by header, group by data window, visual grouping, group hierarchies, etc.), Filtering (filtering by headers, filtering by filter cards, filtering by general tab, filtering by wildcard tab, filtering by condition tab, filtering by top tab, etc.), Problem Demonstration. Introduction to Hierarchies, creating a hierarchy, built-in hierarchies, understanding data granularity, data granularity using marks card, Sorting using toolbar, sorting using pill, sorting using marks card, sorting by legends, Problem Demonstration. Introduction to Data Blending, data blending with Tableau, Problem Demonstration, basics of Joins & Union, Inner Join, Left Outer Join, Right Outer Join, Full Outer Join, Cross Join, Joins vs Blending, Problem Demonstration. Introduction to Calculations in Tableau, types of calculations, ways to create a calculated field, Problem Demonstration, Built-In Functions (Number Function, String Function, Date Function, Logical Function, Aggregate Function, Problem Demonstration. Introduction to Table Calculations, Quick Table Calculation, Tableau Parameters, User Input Analysis, What-If Analysis, Level of Detail Calculations (LOD), LOD Parameters, Fixed LOD Expression, Include LOD Expression, Exclude LOD Expression, LOD use cases and Problem Demonstration. Introduction to Trend Lines and Reference Lines, Creating a Trend Line, Visual Grouping, p-value, R-Squared, Editing Trend Lines, Type of Trend Lines, Linear Trend, Logarithmic Trend, Exponential Trend, Polynomial Trend, Problem Demonstration. Introduction to Forecasting, Forecasting Length, Forecasting Source Data, Forecast Model, Summary Box, Problem Demonstration. Introduction to Mapping, Classification of Maps, Filled Map, Symbol Map, Density Map, Connect to a Spatial File, Interpretation of Spatial Data, Map Views from a Spatial File, Aggregate & Disaggregate Map Views, Working with Additional Data, Map Views for Analysis, Joining Spatial Files, Problem Demonstration. Introduction to Web Mapping Services (WMS), Connect to a WMS Server, WMS Background Map, Problem Demonstration, Compare Chart Items, Static Composition, Correlation, Time Comparison, Distribution, Location, KPI’s. Introduction to Dashboards in Tableau, Dashboard Approaches, Dashboard Interface, Dashboard Objects, Manipulating Objects, Web Page Object, Image Object, Building Dashboard, Problem Demonstration. Introduction to Dashboard Layouts, Containers, Tiled, Floating, Positioning & Sizing, Filtering, Dashboard Formatting, Problem Demonstration, Interactive Dashboards, Types of Actions, Filter Actions, Highlight Actions, URL Actions, Designing Dashboard for Tablets, Designing Dashboards for Mobile Phones, Problem Demonstration. Introduction to Story Points, Creating Story Point, Data Visualization Best Practices, Case Studies and Problem Demonstration.

This Data science and machine learning training program includes a range of project work and exercises to help students apply their learning to real-world problems and build portfolio. The projects and exercises are designed to give students hands-on experience with data analysis, modeling, and communication, and to build their problem-solving skills.