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Data Science using Python Course in Delhi NCR | Certification India


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Courses Overview

Data Science with Python teaches you Python language as a tool for data science, and especially to apply an advanced machine learning algorithm with Python programming.

  • 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 are leading companies using the power of analytics?

    • Critical Success Driver

    • Overview of analysis tools and their popularity

    • Analytics method and problem solving framework

    • List of steps in Analytics projects

    • Identify the most suitable solution design for the given problem statement

    • Project plan for Analytics project and key milestones based on effort projections

    • Create resource plan for analytics project

    • Why Python for Data Science?

    • Overview of Python - Starting with Python

    Introduction to Python Installation

    • Introduction of Python Editors and IDE (Canopy, Pitch, Jupiter, Rodeo, IPHONE etc ...)

    • Understand Jupiter notebooks and customize settings

    • Concept of package / libraries - Important package (NumPy, SciPy, Scikit-learn, Pando, 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?

    ·         Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

    ·         Importing Data from various sources (Csv, txt, excel, access etc)

    ·         Database Input (Connecting to database)

    ·         Viewing Data objects - subsetting, methods

    ·         Exporting Data to various formats

    ·         Important python modules: Pandas, beautifulsoup

    ·         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)

    ·         Introduction exploratory data analysis

    ·         Descriptive statistics, Frequency Tables and summarization

    ·         Universal analysis (distribution of data and graphical analysis)

    • Biwariate analysis (cross tab, distribution and 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)

    ·         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), Anova, Correlations and Chi-square

    Important modules for statistical methods: Stupid, Sissy, Pando

    • The concept of model in analysis and how it is used?

    Common phrasing utilized as a part of investigation and displaying process


    •        Popular displaying calculations


    •        Types of Business issues - Mapping of Techniques


    •        Different Phases of Predictive Modeling


    •        Need for organized exploratory information

    EDA framework (data audit report) to detect data and identify any problem with data

    • Identify missing data

    • Identify the outletors data

    • Visualize the trends and patterns of data

    Data needs to be prepared

    • Consolidation / Aggregation - External Treatment - Flat Liner - Missing Value - Dummy Construction - Convertible Deficiencies

    • Variable reduction techniques - Factor and PCA analysis

    • Introduction to segmentation

    • Types of Segmentation (subjective vs. purpose, heuristics vs. statistical)

    • Hierographic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

    • Behavioral Segmentation Techniques (K-Measure Cluster Analysis)

    • Cluster Evaluation and Profiling - Identify Cluster Features

    ·         Interpretation of results - Implementation on new information


    •        Introduction - Applications


    •        Assumptions of Linear Regression


    •        Building Linear Regression Model


    •        Understanding standard measurements (Variable noteworthiness, R-square/Adjusted R-square, Global theory ,and so on)

    ·         Measure the overall effectiveness of the model

    • Model Verification (vs vs vs scoring)

    • Standard trade output (desil analysis, error distribution (histogram), model equation, driver etc.)

    • Explanation of the results - Business Verification - implementation on new data

    • Introduction - Applications

    • Linear Regression vs. Logistic Regression vs. Normalized Linear Models

    • Construction of Logistic Regression Models (Binary Logistic Model)

    • Understanding the Standard Model Metric (Coordination, Variable Importance, Hosmer Lamesov Test, Guinea, KS, Miscellaneous, 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)

    Explanation of the results - Business Verification - implementation on new data

    • Introduction - Applications

    • Time series components (trend, seasonal, cyclicity and levels) and decomposition

    Classification of Techniques (Pattern Based - Pattern Low)

    • Basic techniques - Avg, Smoothening, etc.

    • Advanced technology - AR model, area, etc.

    ·         Understanding Forecasting Accuracy - MAPE, MAD, MSE, and so on


    •        Introduction to Machine Learning and Predictive Modeling

    Types of Business Problems - Mapping of Techniques - Regression vs. Classification vs. Segmentation vs. Forecasting

    • Major categories of learning algorithms - supervised versus unprivileged learning

    • Various stages of predictive modeling (data preprocessing, sampling, model building, verification)

    • Overfitting (twenty-variance trade off) and performance metrics

    • Feature engineering and dimension reduction

    • Concept of customization and cost function

    • Overview of gradient line algorithms

    • Cross verification overview (bootstrapping, K-fold verification etc.)

    • Model performance metric (R-square, adjusted R-Square, RMSE, MAPE, AUC, ROC curve, memory, accuracy, sensitivity, specificity, illusion metrics)

    • What is the division and role of ML in segmentation?

    • The concept of distance and related math background

    • Meaning Clustering

    • Maximum Expectancy

    • hierarchical clustering

    • Spectral Clustering (DBSCAN)

    • Principle Component Analysis (PCA)

    • Decision Trees - Introduction - Applications

    • Types of Decision Tree Algorithms

    • Construction of decision tree through simplified examples; Selecting the "best" attribute on each non-address node; Entropy; Information Benefits, Guinea Index, Chi Square, Regression Trees

    • making decision tree normal; Information content and profit ratio; Working with numerical variables; Other Remedies of Randomness

    • Cutting a decision tree; Cost as consideration; Unwrapping trees as rules

    • Decision Trees - Verification

    • Overfitting - Best Practices for Avoiding

    • Concept of Ensembling

    • Manual enabling vs. Automatic Ensembling

    ·         Manual enabling vs. Automatic Ensembling

    • Methods of Ensembling (Stacks, Combinations of Experts)

    • Bagging (logic, practical application)

    • Random jungle (logic, practical application)

    • Boosting (Logic, Practical Applications)

    • Eda Boost

    • Gradient Boosting Machines (GBM)

    • XGBoost

    • Inspiration for neural networks and its applications

    • Pseptron and single layer neural networks, and hand calculations

    • Learning in a multi-layered nerve net: Back promotion and compatible gradient techniques

    • Neural network for regression

    Neural network for classification

    • Define output and fine tune model with hyper parameter

    • SVM Model Valid

    • What is KNN and Application?

    • KNN for missing treatment

    • To solve KNN regression problems

    • KNN to solve classification problems

    • validating the KNN model

    • Fix tuning model with hyper parameter

    • The concept of conditional probability

    • Bayes theorem and its applications

    • Naval Bayes for classification

    • Applications of Naive Bayes in classification

    • Big text taming, uncontrolled versus semi-structured data; Information retrieval basics, properties of words; Creating a Term-Document (TxD); Matrice; Equality measures, low-level processes (sentence division) tokenization; Speech-speech-tagging; Stemming; Chanking)

    • Examples and active insights using social media analytics

    • Important Python modules for machine learning (Learn SciKit, Figures Model, Scipy, nltk etc.)

    ·         Interpretation of Outputs and Fine tune the models with hyper parameters

    ·         Validating ANN models

    ·         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)

    ·         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

    Tune the models legitimately utilizing hyper parameters, network seek, funneling and so on.


    Applying diverse calculations to take care of business issues and stamping comes about benchmark


  • Duration: 48 HRS
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