Module 1: Introduction to Information Science
Introduction to the Business & Buzzwords
Industrial utility of information science
Introduction to totally different Information Science Strategies
Essential Software program & Instruments
Profession paths & development in information science
Module 2: Introduction to Excel
Introduction to Excel- Interface, Sorting & Filtering,
Excel Reporting- Fundamental & Conditional Formatting
Layouts, Printing and Securing Recordsdata
Module 3: Introduction to Stats
Introduction to Statistics & It’s Purposes
Intro: Inferential vs. descriptive statistics
Module 4: Descriptive Stats Utilizing Excel Datasets
Categorical Variables Visualization Utilizing Excel Charts- FDT, Pie Charts, Bar Charts & Pareto
Numerical Variables Visualization of Frequency & Absolute Frequency- Utilizing Histogram, Cross Desk & Scatter Plot
Measure of Unfold ( Imply, Mode , Median)
Measure of Variance( Skewness, SD, Variance,
Vary, Coef. Of Variance, Bivariate Evaluation, Covariance & Correlation)
Module 5: Inferential Stats Utilizing Excel Datasets
Introduction to Likelihood
Permutation & Mixtures
Customary Regular distribution
Regular vs. Customary Regular distribution
Confidence Intervals & Z-Rating
Speculation Testing & It’s Varieties
Module 6: Database Design & MySQL
Relational Database principle & Introduction to SQL
Database Creation within the MySQL Workbench
Case Statements, Saved Routines and Cursors
Ø Question Optimisation and Greatest Practices
Ø Drawback-Fixing Utilizing SQL
Module 7: Information Visualization Utilizing Superior Excel
Superior Visualizations- PIVOT Charts, Sparklines, Waterfall Charts
Information Evaluation ToolPak – Regression in Excel
Module 8: Information Visualization Utilizing Tableau
Tableau vs Excel and PowerBI
Exploratory and Explanatory Evaluation
Getting began with Tableau
Visualizing and Analyzing information with Tableau – I
Visualizing and Analyzing Information with Tableau – II
Numeric and String capabilities
Logical and Date capabilities
Histograms and parameters
High N Parameters and Calculated Fields
Dashboards – II and Filter Actions
Module 9: Python Programming
Putting in Anaconda & Fundamentals of Python
Introduction to programming languages
Getting Began With Python
Introduction to jupyter Notebooks
Understanding what are capabilities
Defining and calling capabilities
Native and world variables
Various kinds of arguments
Map,scale back,filter,lambda and recursive capabilities
Information Buildings in Python
Operator Enter and Output
Totally different Arithmetic , logical and Relational operators
Break , proceed and Go assertion
Record and dictionary comprehensions
Understanding what are capabilities
Defining and calling capabilities
Native and world variables
Various kinds of arguments
Map,scale back,filter,lambda and recursive capabilities
Totally different perform in file dealing with (open,learn, write,shut)
Totally different modes (r,w,a,r+,w+,a+)
Exception Dealing with, OOPX & Regex
What’s exception dealing with
Attempt, besides, else and at last block
Various kinds of Exception
Totally different capabilities in Regex
Module 10: Python For Information Science
Operations Over 1-D Arrays
Mathematical Operations on NumPy
Mathematical Operations on NumPy II
Computation Occasions in NumPy vs Python Lists
Pandas – Rows and Columns
Groupby and Combination Features
Module 11: Information Visualization Utilizing Python- Matplotlib & Seaborn
Introduction to Information Visualisation with Matplotlib
Introduction to Matplotlib
The Necessity of Information Visualisation
Visualisations – Some Examples
Information Visualisation: Case Research
Information Dealing with and Cleansing: I
Information Dealing with and Cleansing: II
Outliers Evaluation with Boxplots
Information Visualization with Seaborn
Pie – Chart and Bar Chart
Revisiting Bar Graphs and Field Plots
Module 12: Exploratory Information Evaluation
Fixing the Rows and Columns
Impute/Take away Lacking Values
Fixing Invalid Values and Filter Information
Introduction to Univariate Evaluation
Categorical Unordered Univariate Evaluation
Categorical Ordered Univariate Evaluation
Statistics on Numerical Options
Bivariate and Multivariate Evaluation
Numeric – Numeric Evaluation
Numerical – Categorical Evaluation
Categorical – Categorical Evaluation
Module 13: Supervised Studying Mannequin – Regression
Introduction to Easy Linear Regression
Introduction to Easy Linear Regression
Introduction to machine studying
Energy of easy linear regression
Easy linear regression in python
Assumptions of easy linear regression
Studying and understanding the information
Speculation testing in linear regression
Residue evaluation and predictions
Linear Regression utilizing SKLearn
A number of Linear Regression
Motivation-when one variable isn’t sufficient
Shifting from SLR to MLR-new issues
Coping with categorical variables
Mannequin evaluation as compared
A number of Linear Regression in Python
Studying and understanding the information
Constructing the mannequin I & II
Residue evaluation and predictions
Variable choice utilizing RFE
Business Relevance of Linear Regression
Linear regression revision
Prediction versus projection
Exploratory information evaluation
Mannequin constructing – I, II & III
Module 14: Supervised Studying Mannequin – Classification
Univariate Logistic Regression
Discovering the perfect match sigmoid curve – I
Discovering the perfect match sigmoid curve – II
Multivariate Logistic Regression – Mannequin Constructing
Multivariate Logistic Regression – Mannequin Constructing
Information cleansing and preparation – I & II
Constructing your first mannequin
Characteristic elimination utilizing RFE
Confusion metrics and accuracy
Handbook characteristic elimination
Multivariate Logistic Regression – Mannequin Analysis
Multivariate Logistic Regression – Mannequin Analysis
Metrics past accuracy-sensitivity and specificity
Sensitivity and specificity in Python
Discovering the optimum threshold
Mannequin analysis metrics – train
Logistic Regression – Business Purposes – Half I
Getting conversant in logistic regression
Nuances of logistic regression-sample choice
Nuances of logistic regression-segmentation
Nuances of logistic impression-variable transformation-I, II & III
Logistic Regression: Business Purposes – Half II
Mannequin analysis – A re-assessment
Mannequin validation and significance of stability
Monitoring of mannequin efficiency over time
Logistic Regression – Business Purposes – Half II
Generally face challenges in implementation of logistic regression
Mannequin analysis – A re-assessment
Mannequin validation and significance of stability
Monitoring of mannequin efficiency over time
Module 15: Superior Machine Studying
Unsupervised Studying: Clustering
Introduction to Clustering
Executing Ok Means in Python
Introduction to Enterprise Drawback Fixing
Case Research Demonstrationchurn instance
Introduction to Determination Timber
Algorithms for Determination Tree Development
Hyperparameter Tuning in Determination Timber
Ensembles and Random Forests
Time Collection Forecasting – I (BA)
Introduction to Time Collection
Time Collection Forecasting – II (BA)
Introduction to AR Fashions
Ideas of Mannequin Choice
Mannequin Constructing and Analysis
Module 16: AI- NLP, Neural Networks & Deep Studying
Historical past and evolution of NLP
Corpus and Corpus Linguistics
Introduction to the NLTK toolkit
Preprocessing textual content information with NLTK
Fundamental NLP duties utilizing NLTK (e.g., Half-ofSpeech Tagging, Named Entity Recognition)
Stemming and Lemmatization
Sentiment Evaluation with NLTK
Tokenization and Subject Modeling
Bag-of-Phrases illustration
Sentiment Evaluation Undertaking:
Introduction to Sentiment Evaluation
Sentiment Evaluation utilizing supervised and unsupervised strategies
Constructing a Sentiment Evaluation mannequin with Python
Evaluating Sentiment Evaluation fashions
AI vs Deep Studying vs ML
Introduction to Synthetic Intelligence (AI), Machine Studying (ML) and Deep Studying (DL)
Purposes of AI, ML, and DL
Variations between AI, ML and DL
The Idea of Neural Networks
Introduction to Neural Networks
Layers in Neural Networks
Neural Networks – Feed-forward, Convolutional, Recurrent
Feed-forward Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Purposes of Neural Networks
Constructing a Deep Studying mannequin with Python
Picture Classification with Convolutional Neural Networks
Pure Language Processing with Recurrent Neural Networks