SkillDegree in Data Analytics

• Introduction to Data Science with Python ( Induction)

  • What is Analytics & Data Science?
  • Common Terms in Analytics
  • What is data
  • Classification of data
  • 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
  • Why Python for data science?

• Python Core

  • Introduction to python and comparison with other programming language
  • Installation of Anaconda Distribution and other python IDE
  • Python Objects ,Numbers & Booleans, Strings, Container objects, Mutability of objects
  • Concept of Packages/Libraries – Important packages(Pandas, NumPy, SciPy, Scikit-learn, Seaborn, Matplotlib)
  • Operators – Arithmetic, Bitwise, Comparison and Assignment Operators
  • Conditions (If else, if-elif)
  • Loops( While,For)
  • Break and Continue Statement
  • Range Functions

• String Objects and Collection

  • String Object Basics
  • String Methods
  • Splitting and joining strings
  • String Format Functions
  • List object Basics
  • List Methods

• Tuples, Set, Dictionaries & Functions

  • Tuples, Sets, Dictionary object basics, Dictionary, Object methods, Dictionary
  • view objects
  • Functions basics, Iterators
  • Generator functions
  • Lambda Functions
  • Map, Reduce & filter functions

• OOPS concepts & Working with Files

  • OOPS basic concepts
  • Creating classes and objects
  • Working with files
  • Reading and Writing files

• Database

  • MongoDB
  • Python SQL

• Python Pandas

  • Python pandas – series
  • Python pandas – Dataframe
  • Python pandas – Basic Functionality
  • Python pandas- Reindexing
  • Python pandas – Iteration
  • Python pandas – Sorting
  • Python pandas – Working with Text data
  • Python pandas – options and customization
  • Python pandas – Indexing and selecting data
  • Python pandas – Date Functionality
  • Python pandas – Categorical Data
  • Python pandas – Visulaization

• Data Manipulation : Cleansing - Munging

  • Cleansing Data with Python
  • Filling missing values using lambda function and concept of Skewness
  • Data Manipulation steps (Sorting, filtering, duplicates, merging, appending subsetting, derived variables, sampling, Data type conversions, renaming, v formatting 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

  • Descriptive Statistics
  • Sample vs Population Statistics
  • Random variables
  • Probability distribution functions
  • Expected value
  • Normal distribution
  • Gaussian distribution
  • Z-score
  • Central limit theorem
  • Spread and Dispersion
  • Hypothesis testing
  • Z-stats vs T-stats
  • Type 1 & Type 2 error
  • Confidence Interval
  • ANOVA Test
  • Chi Square Test
  • T-test 1-Tail 2-Tail Test
  • Correlation and Covariance

• Introduction To Predictive Modeling

  • Concept of model in analytics and how it is used?
  • Common terminology used in Analytics & Modeling process
  • Popular Modeling algorithms
  • Types of Business problems – Mapping of Techniques
  • Different Phases of Predictive Modeling

• Data Exploration For Modeling

  • Need for structured exploratory data
  • EDA framework for exploring the data and identifying any problems with the v data by the help of pairplot.
  • Identify missing data
  • Identify outliers data
  • Visualize the data trends and patterns

• Data Preparation

  • Merging
  • Normalizing the data
  • Feature Engineering
  • Feature Selection
  • Feature scaling using Standard Scaler.
  • Label encoding
  • Need of Data preparation
  • Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values
  • Dummy creation – Variable Reduction
  • Variable Reduction Techniques – Factor & PCA Analysis

• Tableau

  • Working with tableau
  • Data organisation
  • Creation of parameters
  • Advanced visualization
  • Dashboard data presentation

• Projects

  • Managing credit card Risks
  • Bank Loan default classification
  • Youtube Viewers predictions
  • Super store Analytics (E-commerce)
  • Buying and selling cars prediction(like OLX process)
  • Advanced House price prediction
  • Analytics on HR decisions
  • Twitter Analysis
  • AI Chat Box
  • Flight price prediction