Data Science Certification Training

---

Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.

Topics:

• Python Statistics for Data Science
• Databases – MySQL and SQL Queries
• Data Science Master’s Program
• Machine Learning
• Deep Learning
• Power BI
• Hadoop- Apache Spark and Scala Certification
• Tableau - For Data Visualization
• Data Science Masters - Live Projects

---

Introduction To Python

• Installation and Working with Python
• Understanding Python variables
• Python basic Operators
• Understanding the Python blocks.

Introduction To Variables

• Variables, expression condition and function
• Global and Local Variables in Python
• Packing and Unpacking Arguments
• Type Casting in Python
• Byte objects vs. string in Python
• Variable Scope

Python Data Type

• Declaring and usingNumeric data types
• Using stringdata type and string operations
• Understanding Non-numeric data types
• Understanding the concept of Casting and Boolean.
• Strings
• List
• Tuples
• Dictionary
• Sets

Introduction Keywords and Identifiers and Operators

• Python Keyword and Identifiers
• Python Comments, Multiline Comments.
• Python Indentation
• Understating the concepts of Operators

List

• What is List.
• List Creation
• List Length
• List Append
• List Insert
• List Remove
• List Append & Extend using “+” and Keyword
• List Delete
• List related Keyword in Python
• List Revers
• List Sorting
• List having Multiple Reference
• String Split to create a List
• List Indexing
• List Slicing
• List count and Looping
• List Comprehension and Nested Comprehension

Dictionary

• Dict Creation
• Dict Access (Accessing Dict Values)
• Dict Get Method
• Dict Add or Modify Elements
• Dict Copy
• Dict From Keys.
• Dict Items
• Dict Keys (Updating, Removing and Iterating)
• Dict Values
• Dict Comprehension
• Default Dictionaries
• Ordered Dictionaries
• Looping Dictionaries
• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets, Tuples and Looping Programming

Sets

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations
• Frozen Sets

Tuple

• What is Tuple
• Tuple Creation
• Accessing Elements in Tuple
• Changinga Tuple
• TupleDeletion
• Tuple Count
• Tuple Index
• TupleMembership
• TupleBuilt in Function (Length, Sort)

Control Flow

• Loops
• Loops and Control Statements (Continue, Break and Pass).
• Looping techniques in Python
• How to use Range function in Loop
• Programs for printing Patterns in Python
• How to use if and else with Loop
• Use of Switch Function in Loop
• Elegant way of Python Iteration
• Generator in Python
• How to use nested IF and Else in Python
• How to use nested Loop in Python
• Use If and Else in for and While Loop
• Examples of Looping with Break and Continue Statements
• How to use IN or NOTkeywordin Python Loop.

Exception and File Handling, Module, Function and Packages

Python Exception Handling

• Python Errors and Built-in-Exceptions
• Exception handing Try, Except and Finally
• Catching Exceptions in Python
• Catching Specific Exception in Python
• Raising Exception
• Try and Finally

Python File Handling

• Opening a File
• Python File Modes
• Closing File
• Writing to a File
• Reading from a File
• Renaming and Deleting Files in Python
• Python Directory and File Management
• List Directories and Files
• Making New Directory
• Changing Directory

Python Function, Modules and Packages

• Python Syntax
• Function Call
• Return Statement
• Write an Empty Function in Python –pass statement.
• Lamda/ Anonymous Function
• *argsand **kwargs
• Help function in Python
• Scope and Life Time of Variable in Python Function
• Nested Loop in Python Function
• Recursive Function and Its Advantage and Disadvantage
• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Programming using functions, modules & external packages
• Map, Filter and Reduce function with Lambda Function
• More example of Python Function

Data Automation (Excel, SQL, PDF etc)

Python Object Oriented Programming—Oops

• Concept of Class, Object and Instances
• Constructor, Class attributes and Destructors
• Real time use of class in live projects
• Inheritance, Overlapping and Overloading operators
• Adding and retrieving dynamic attributes of classes
• Programming using Oops support

Python Database Interaction

• SQL Database connection using
• Creating and searching tables
• Reading and Storing configinformation on database
• Programming using database connections

Reading an excel

• Reading an excel file usingPython
• Writing toan excel sheet using Python
• Python| Reading an excel file
• Python | Writing an excel file
• Adjusting Rows and Column using Python
• ArithmeticOperation in Excel file.
• Plotting Pie Charts
• Plotting Area Charts
• Plotting Bar or Column Charts using Python.
• Plotting Doughnut Chartslusing Python.
• Consolidationof Excel File using Python
• Split of Excel File Using Python.
• Play with Workbook, Sheets and Cells in Excel using Python
• Creating and Removing Sheets
• Formatting the Excel File Data
• More example of Python Function

Working with PDF and MS Word using Python

• Extracting Text from PDFs
• Creating PDFs
• Copy Pages
• Split PDF
• Combining pages from many PDFs
• Rotating PDF’s Pages

Complete Understanding of OS Module of Python

• Check Dirs. (exist or not)
• How to split path and extension
• How to get user profile detail
• Get the path of Desktop, Documents, Downloads etc.
• Handle the File System Organization using OS
• How to get any files and folder’s details using OS

Data Analysis & Visualization

Pandas

• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
• How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
• Exploring the Data Plotting, Correlations, and Histograms
• Getting statistical information about the data Analysis Concepts, Handle the None Values
• Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
• Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
• Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
• Exporting the results to Excel Joins:
• Python | Pandas Data Frame Inner Join
• Under sting the properties of Data Frame Left Join (Left Outer Join)
• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
• Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
• Pandas | How to Group Data How to apply Lambda / Function on Data Frame
• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

NumPy

• Introduction to NumPy: Numerical Python
• Importing NumPy and Its Properties
• NumPy Arrays
• Creating an Array from a CSV
• Operations an Array from aCSV
• Operations with NumPy Arrays
• Two-Dimensional Array
• Selecting Elements from 1-D Array
• Selecting Elements from 2-D Array
• Logical Operation with Arrays
• Indexing NumPy elements using conditionals
• NumPy’sMean and Axis
• NumPy’sMode, Median and Sum Function
• NumPy’sSort Function and More

MatPlotLib

• Bar Chart using Python MatPlotLib
• Column Chart using Python MatPlotLib
• Pie Chart using Python MatPlotLib
• Area Chart using Python MatPlotLib
• Scatter Plot Chart using Python MatPlotLib
• Play with Charts Properties Using MatPlotLib
• Export the Chart as Image
• Understanding plt. subplots () notation
• Legend Alignment of Chart using MatPlotLib
• Create Charts as Image
• Other Useful Properties of Charts.
• Complete Understanding of Histograms
• Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Seaborn

• Introduction to Seaborn
• Making a scatter plot with lists
• Making a count plot with a list
• Using Pandas with seaborn
• Tidy vs Untidy data
• Making a count plot with a Dataframe
• Adding a third variable with hue
• Hue and scattera plots
• Hue and count plots

Visualizing Two Quantitative Variables

• Introduction to relational plots and subplots
• Creating subplots with col and row
• Customizing scatters plots
• Changing the size of scatter plot points
• Changing the style of scatter plot points
• Introduction to line plots
• Interpreting line plots
• Visualizing standard deviation with line plots
• Plotting subgroups in line plots

Visualizing a Categorical and a Quantitative Variable

• Current plots and bar plots
• Count plots
• Bar plot with percentages
• Customizing bar plots
• Box plots
• Create and interpret a box plot
• Omitting outliers
• Adjusting the whiskers
• Point plots
• Customizing points plots
• Point plot with subgroups

Customizing Seaborn Plots

• Changing plot style and colour
• Changing style and palette
• Changing the scale
• Using a custom palette
• Adding titles and labels: Part 1
• Face Grids vs. Axes Subplots
• Adding a title to a face Grid object
• Adding title and labels: Part 2
• Adding a title and axis labels
• Rotating x-tics labels
• Putting it all together
• Box plot with subgroups
• Bar plot with subgroups and subplots

Get full course syllabus in your inbox

Download Curriculum

---

Python - MySQL

• Introduction to MySQL
• What is the MySQLdb
• How do I Install MySQLdb
• Connecting to the MYSQL
• Selecting a database
• Adding data to a table
• Executing multiple queries
• Exporting and Importing data tables.

SQL Functions

• Single Row Functions
• Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date

General Functions

• Count, Average, Sum, Now etc.

Joining Tables

• Obtaining data from Multiple Tables
• Types of Joins (Inner Join, Left Join, Right Join & Full Join)
• Sub-Queries Vs. Joins

Operators (Data using Group Function)

• Distinct, Order by, Group by, Equal to etc.

Database Objects (Constraints & Views)

• Not Null
• Unique
• Primary Key
• Foreign Key

SQL Basic

• SQL Introduction
• SQL Syntax
• SQL Select
• SQL Distinct
• SQL Where
• SQL And & Or
• SQL Order By
• SQL Insert
• SQL Update
• SQL Delete

SQL Advance

• SQL Like
• SQL Wildcards
• SQL In
• SQL Between
• SQL Alias
• SQL Joins
• SQL Inner Join
• SQL Left Join
• SQL Right Join
• SQL Full Join
• SQL Union

SQL Functions

• SQL Avg()
• SQL Count()
• SQL First()
• SQL Last()
• SQL Max()
• SQL Min()
• SQL Sum()
• SQL Group By

---

Introduction to Data Science

• 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

Accessing/Importing and Exporting Data

• Importing Data from various sources (Csv, txt, excel, access etc)
• Database Input (Connecting to database)
• Viewing Data objects - sub setting, methods
• Exporting Data to various formats
• Important python modules: Pandas

Data Manipulation: Cleansing - Munging Using Python Modules

• Cleansing Data with Python
• Filling missing values using lambda function and concept of Skewness.
• Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
• Normalizing data
• Feature Engineering
• Feature Selection
• Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
• Label encoding/one hot encoding

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 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
• Inferential Statistics-Sampling
• 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 Co-variance

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
• Imbalanced Data Techniques

Data Pre-Processing & Data Mining

• Data Preparation
• Feature Engineering
• Feature Scaling
• Datasets
• Dimensionality Reduction
• Anomaly Detection
• Parameter Estimation
• Data and Knowledge
• Selected Applications in Data Mining

---

Introduction to Machine Learning

• Artificial Intelligence
• Machine Learning
• Machine Learning Algorithms
• Algorithmic models of Learning
• Applications of Machine Learning
• Large Scale Machine Learning
• Computational Learning theory
• Reinforcement Learning

Techniques of Machine Learning

• Supervised Learning
• Unsupervised Learning
• Semi-supervised and Reinforcement Learning
• Bias and variance Trade-off
• Representation Learning

Regression

• Regression and its Types
• Logistic Regression
• Linear Regression
• Polynomial Regression

Classification

• Meaning and Types of Classification
• Nearest Neighbor Classifiers
• K-nearest Neighbors
• Probability and Bayes Theorem
• Support Vector Machines
• Naive Bayes
• Decision Tree Classifier
• Random Forest Classifier

Unsupervised Learning: Clustering

• About Clustering
• Clustering Algorithms
• K-means Clustering
• Hierarchical Clustering
• Distribution Clustering