Data Science and Machine Learning with Python is a field of study and practice that combines data analysis, statistical modeling, and machine learning techniques using the Python programming language. Data Science and Machine Learning with Python involve the use of Python programming along with statistical analysis and machine learning techniques to extract insights, build predictive models, and solve complex data-related problems across various domains, including finance, healthcare, marketing, and more.
A huge library ecosystem. Python offers a vast choice of libraries for AI development, which contain base-level items that save coding time. High readability. The flexibility of the language. Abundant community support. Excellent visualization options.
Introduction to Data Science with Python
What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project
Python Essentials
Why Python for data science?
Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical – string – date
Reading and writing data
Simple plotting
Control flow & conditional statements
Debugging & Code profiling
How to create class and modules and how to call them?
Scientific Distributions Used In Python For Data Science
NumPy, pandas, scikit-learn, stat models, nltk
Accessing/Importing And Exporting Data Using Python Modules
Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects – subsetting Data, methods
Exporting Data to various formats
Important python modules: Pandas, beautiful soup
Data Manipulation – Cleansing – Munging using python modules
Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
Data Analysis – Visualization Using Python
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc)
Introduction to Statistics
Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests( One sample, independent, paired), Analysis of variance, Correlations and Chi-square
Important modules for statistical methods: NumPy, SciPy, Pandas
Introduction to Predictive Modelling
Concept of model in analytics and how it is used?
Common terminology used in analytics & Modelling process
Popular modelling algorithms
Types of Business problems – Mapping of Techniques
Different Phases of Predictive Modelling
Data Exploration For Modelling
Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns
Data Preparation
Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis
Segmentation: Solving Segmentation Problems
Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioural Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling – Identify cluster characteristics
Interpretation of results – Implementation on new data
Linear Regression: Solving Regression Problems
Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results – Business Validation – Implementation on new data
Logistic Regression : Solving Classification Problems
Introduction – Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results – Business Validation – Implementation on new data
Time Series Forecasting : Solving Forecasting Problems
Introduction – Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based – Pattern less)
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
Machine Learning : Predictive Modelling
Introduction to Machine Learning & Predictive Modelling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
Unsupervised Learning : Segmentation
What is segmentation & Role of ML in Segmentation?
Concept of Distance and related math background
K-Means Clustering
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principle component Analysis (PCA)
Supervised Learning :- Decision Trees
Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees – Validation
Overfitting – Best Practices to avoid
Supervised Learning :- Ensemble Learning
Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost
Supervised Learning :- Artificial Neural Network – ANN
Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models
Supervised Learning :- Support Vector Machines
Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models
Supervised Learning :-KNN
What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters
Supervised Learning :- Naive Bayes
Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications
Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using Python
Text Analytics – Word cloud analysis using Python
Text Analytics – Segmentation using K-Means/Hierarchical Clustering
Text Analytics – Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.
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1. Basic user account management (creating, modifying, and deleting users).
2. Password resets and account unlocks.
3. Basic file system navigation and management (creating, deleting, and modifying files and directories).
4. Basic troubleshooting of network connectivity issues.
5. Basic software installation and package management (installing and updating software packages).
6. Viewing system logs and checking for errors or warnings.
7. Running basic system health checks (CPU, memory, disk space).
8. Restarting services or daemons.
9. Monitoring system performance using basic tools (top, df, free).
10. Running basic commands to gather system information (uname, hostname, ifconfig).
1. Intermediate user account management (setting permissions, managing groups).
2. Configuring network interfaces and troubleshooting network connectivity issues.
3. Managing file system permissions and access control lists (ACLs).
4. Performing backups and restores of files and directories.
5. Installing and configuring system monitoring tools (Nagios, Zabbix).
6. Analyzing system logs for troubleshooting purposes.
7. Configuring and managing software repositories.
8. Configuring and managing system services (systemd, init.d).
9. Performing system updates and patch management.
10. Monitoring and managing system resources (CPU, memory, disk I/O).
1. Advanced user account management (LDAP integration, single sign-on).
2. Configuring and managing network services (DNS, DHCP, LDAP).
3. Configuring and managing storage solutions (RAID, LVM, NFS).
4. Implementing and managing security policies (firewall rules, SELinux).
5. Implementing and managing system backups and disaster recovery plans.
6. Configuring and managing virtualization platforms (KVM, VMware).
7. Performance tuning and optimization of system resources.
8. Implementing and managing high availability solutions (clustering, load balancing).
9. Automating system administration tasks using scripting (Bash, Python).
10. Managing system configurations using configuration management tools (Ansible, Puppet).
1. Learning basic shell scripting for automation tasks. 2. Understanding file system permissions and ownership. 3. Learning basic networking concepts (IP addressing, routing). 4. Learning how to use package management tools effectively. 5. Familiarizing with common Linux commands and utilities. 6. Understanding basic system architecture and components. 7. Learning basic troubleshooting techniques and methodologies. 8. Familiarizing with basic security principles and best practices. 9. Learning how to interpret system logs and diagnostic output. 10. Understanding the role and importance of system backups and restores.
1. Advanced scripting and automation techniques (error handling, loops).
2. Understanding advanced networking concepts (VLANs, subnetting).
3. Familiarizing with advanced storage technologies (SAN, NAS).
4. Learning advanced security concepts and techniques (encryption, PKI).
5. Understanding advanced system performance tuning techniques.
6. Learning advanced troubleshooting methodologies (root cause analysis).
7. Implementing and managing virtualization and cloud technologies.
8. Configuring and managing advanced network services (VPN, IDS/IPS).
9. Implementing and managing containerization technologies (Docker, Kubernetes).
10. Understanding enterprise-level IT governance and compliance requirements.
1. Designing and implementing complex IT infrastructure solutions. 2. Architecting and implementing highly available and scalable systems. 3. Developing and implementing disaster recovery and business continuity plans. 4. Conducting security audits and vulnerability assessments. 5. Implementing and managing advanced monitoring and alerting systems. 6. Developing custom automation solutions tailored to specific business needs. 7. Providing leadership and mentorship to junior team members. 8. Collaborating with other IT teams on cross-functional projects. 9. Evaluating new technologies and making recommendations for adoption. 10. Participating in industry conferences, workshops, and training programs.
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