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Data Science Master Program Certification Training Course
Curriculum
52 Sections
506 Lessons
16 Weeks
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PYTHON
0
Introduction to Python
5
2.1
Python programming history & features
2.2
Python compiler and IDE installation
2.3
Virtual Environment
2.4
Pip – Package Manager
2.5
Hands-on
Basics of Python
7
3.1
Python Syntax Overview, Indentation, comments
3.2
Variable declaration
3.3
Datatypes and data structure
3.4
Primitive
3.5
Non-primitive
3.6
Operators in python
3.7
Hands-on
Program flow/ Data flow of Python
15
4.1
Conditional Statements
4.2
if statement
4.3
if … else statement
4.4
if … elif… else statement
4.5
Looping
4.6
for loop
4.7
for with else statement
4.8
while loop
4.9
while with else statement
4.10
Control Statements
4.11
break
4.12
Continue
4.13
pass
4.14
Assert Statement
4.15
Hands-on
Function in Python
6
5.1
Syntax of Function
5.2
Function with *args & **kwargs
5.3
Scope of variables
5.4
Lambda function with map, filter, reduce method
5.5
DocString
5.6
Modules and standard Module
File Handling in Python
10
6.1
File Opening modes
6.2
Context Manager in python
6.3
File Operations
6.4
Open
6.5
Create
6.6
Read
6.7
Write
6.8
Update
6.9
Delete
6.10
Hands-on
Exception Handling in Python
9
7.1
Types of Errors in python
7.2
Exception handling with
7.3
try … except
7.4
try … except… finally
7.5
try … except… else
7.6
Multiple Exception
7.7
Raising Exception
7.8
User-defined Exception
7.9
Hands-on
Oops in Python
8
8.1
Oops Concepts with programming syntax
8.2
Class
8.3
Object
8.4
Polymorphism
8.5
Encapsulation
8.6
Inheritance
8.7
Types of Methods in python
8.8
Hands-on
Core Concepts in Python
4
9.1
Iterator
9.2
Generator
9.3
Decorator
9.4
Hands-on
Comprehension in Python
11
10.1
Comprehensions
10.2
List
10.3
Nested List
10.4
if statement
10.5
if … else statement
10.6
Nested if … else statement
10.7
Dictionary
10.8
Sorting
10.9
List
10.10
Dictionary
10.11
Hands-on
Thread and DateTime in Python
11
11.1
Terms in threading
11.2
process
11.3
thread
11.4
multithreading
11.5
Time complexity
11.6
Thread Life cycle
11.7
Programming with Threading & Multithreading
11.8
Synchronization
11.9
Sleep and execution time of code
11.10
DateTime module
11.11
Hands-on
Advanced data Structure/ collections in Python
7
12.1
Deque
12.2
namedtuple
12.3
ChainMap
12.4
Counter
12.5
Ordered Dictionary
12.6
Default Dictionary
12.7
Hands-on
MySQL with Python
10
13.1
SQL statements & Operations
13.2
Create
13.3
Read
13.4
Update
13.5
Delete
13.6
Python – SQL connector package installation
13.7
Python with CRUD Operations
13.8
Commit & Rollback
13.9
SQL Related Exception Handling
13.10
Hands-on
Network programming with Python
5
14.1
Terms and Basics of network programming
14.2
The architecture of data transmission between sender and receiver using python
14.3
Getting data from the remote server
14.4
Client & Server-side programming
14.5
Hands-on
Regular Expression with Python
8
15.1
Regex Syntax
15.2
Quantifiers
15.3
Metacharacters
15.4
Special Sequences
15.5
Sets
15.6
Python re module
15.7
Methods with regex usage
15.8
Hands-on
GUI programming with Python
9
16.1
Introduction
16.2
Components and Events
16.3
An Example GUI
16.4
Widgets
16.5
Layout Management
16.6
Signals & Slots
16.7
QMessagesBox, QDialog
16.8
Database Handling
16.9
Hands-on
API access with Python
4
17.1
Google Text to Speech
17.2
Google Speech to Text
17.3
OpenWeatherMap
17.4
Hands-on
DataScience with Python
4
18.1
Pandas – Series and Dataframe
18.2
Numpy
18.3
Matplotlib
18.4
Hands-on
The project with Python
4
19.1
Creating own application with any one of the frameworks
19.2
Django App
19.3
PyQt5 App
19.4
Console oriented Core app
Machine Learning
0
SupervisedLearning
12
21.1
Linear Regression
21.2
Linear Equation
21.3
Slope
21.4
Intercept
21.5
R square value
21.6
Logistic regression
21.7
ODDS ratio
21.8
Probability of success
21.9
Probability of failure Bias Variance Tradeoff
21.10
ROC curve
21.11
Bias Variance Tradeoff
21.12
Hands-on: we’ve reviewed the main ways to approach the problem of modeling data using simple and definite
Unsupervised Learning
3
22.1
K-Means
22.2
K-Means ++
22.3
Hierarchical Clustering
SVM
4
23.1
Support Vectors
23.2
Hyperplanes
23.3
2-D Case
23.4
Linear Hyperplane
SVM Kernal
3
24.1
Linear
24.2
Radial
24.3
Polynomial
Other Machine Learning algorithms
8
25.1
K – Nearest Neighbour
25.2
Naïve Bayes Classifier
25.3
Decision Tree – CART
25.4
Decision Tree – C50
25.5
Random Forest
25.6
Hands-on: We have covered the simplest but still very practical machine learning models in an eminently practical way to get us started on the complexity
25.7
Hands-on: where we will cover several regression techniques, it will be time to go and solve a new type of problem that we have not worked on, even if it’s possible to solve the problem with clustering methods (regression), using new mathematical tools for approximating unknown values.
25.8
Hands-on: In it, we will model past data using mathematical functions, and try to model new output based on those modeling.
Deep Learning
0
Deep Learning Algorithms
5
27.1
CNN – Convolutional Neural Network
27.2
RNN – Recurrent Neural Network
27.3
ANN – Artificial Neural Network
27.4
Hands-on: We took a very important step toward solving complex problems together by means of implementing our first neural
27.5
Hands-on: Now, the following architectures will have familiar elements, and we will be able to extrapolate the knowledge acquired in this chapter, into novel
Introduction to NLP
6
28.1
Text Pre-processing
28.2
Noise Removal
28.3
Lexicon Normalization
28.4
Lemmatization
28.5
Stemming
28.6
Object Standardization
Text to Features (Feature Engineering)
10
29.1
Syntactical Parsing
29.2
Dependency Grammar
29.3
Part of Speech Tagging
29.4
Entity Parsing
29.5
Named Entity Recognition
29.6
Topic Modelling
29.7
N-Grams
29.8
TF – IDF
29.9
Frequency / Density Features
29.10
Word Embedding’s
Tasks of NLP
7
30.1
Text Classification
30.2
Text Matching
30.3
Levenshtein Distance
30.4
Phonetic Matching
30.5
Flexible String Matching
30.6
Hands-on: provided, you will even be able to create new customized
30.7
Hands-on: As our models won’t be enough to solve very complex problems, in the following chapter, our scope will expand even more, adding the important dimension of time to the set of elements included in our generalization.
Artificial Intelligence
0
AI Introduction
5
32.1
Perceptron
32.2
Multi-Layer Perceptron
32.3
Markov Decision Process
32.4
Logical Agent & First Order Logic
32.5
AL Applications
Power BI
0
Introduction to Power BI
11
34.1
Get Started with Power BI
34.2
Overview: Power BI concepts
34.3
Sign up for Power BI
34.4
Overview: Power BI data sources
34.5
Connect to a SaaS solution
34.6
Upload a local CSV file
34.7
Connect to Excel data that can be refreshed
34.8
Connect to a sample
34.9
Create a Report with Visualizations
34.10
Explore the Power BI portal
34.11
Hands-On
Viz and Tiles
11
35.1
Overview: Visualizations
35.2
Using visualizations
35.3
Create a new report
35.4
Create and arrange visualizations
35.5
Format a visualization
35.6
Create chart visualizations
35.7
Use text, map, and gauge visualizations and save a report
35.8
Use a slicer to filter visualizations
35.9
Sort, copy, and paste visualizations
35.10
Download and use a custom visual from the gallery
35.11
Hands-On
Reports and Dashboards
21
36.1
Modify and Print a Report
36.2
Rename and delete report pages
36.3
Add a filter to a page or report
36.4
Set visualization interactions
36.5
Print a report page
36.6
Send a report to PowerPoint
36.7
Create a Dashboard
36.8
Create and manage dashboards
36.9
Pin a report tile to a dashboard
36.10
Pin a live report page to a dashboard
36.11
Pin a tile from another dashboard
36.12
Pin an Excel element to dashboard
36.13
Manage pinned elements in Excel
36.14
Add a tile to a dashboard
36.15
Build a dashboard with Quick Insights
36.16
Set a Featured (default) dashboard
36.17
Ask Questions about Your Data
36.18
Ask a question with Power BI Q&A
36.19
Tweak your dataset for Q&A
36.20
Enable Cortana for Power BI
36.21
Hands-On
Publishing Workbooks and Workspace
10
37.1
Share Data with Colleagues and Others
37.2
Publish a report to the web
37.3
Manage published reports
37.4
Share a dashboard
37.5
Create an app workspace and add users
37.6
Use an app workspace
37.7
Publish an app
37.8
Create a QR code to share a tile
37.9
Embed a report in SharePoint Online
37.10
Hands-On
Other Power BI Components and Table Relationship
14
38.1
Use Power BI Mobile Apps
38.2
Get Power BI for mobile
38.3
View reports and dashboards in the iPad app
38.4
Use workspaces in the mobile app
38.5
Sharing from Power BI Mobile
38.6
Use Power BI Desktop
38.7
Install and launch Power BI Desktop
38.8
Get data
38.9
Reduce data
38.10
Transform data
38.11
Relate tables
38.12
Get Power BI Desktop data with the Power BI service
38.13
Export a report from Power BI service to Desktop
38.14
Hands-On
DAX functions
10
39.1
New Dax functions
39.2
Date and time functions
39.3
Time intelligence functions
39.4
Filter functions
39.5
Information functions
39.6
Logical functions
39.7
Math & trig functions
39.8
Parent and child functions
39.9
Text functions
39.10
Hands-On
Tableau
10
40.1
Start Page
40.2
Show Me
40.3
Connecting to Excel Files
40.4
Connecting to Text Files
40.5
Connect to Microsoft SQL Server
40.6
Connecting to Microsoft Analysis Services
40.7
Creating and Removing Hierarchies
40.8
Bins
40.9
Joining Tables
40.10
Data Blending
Learn Tableau Basic Reports
13
41.1
parameters
41.2
Grouping Example 1
41.3
Grouping Example 2
41.4
Edit Groups
41.5
Set
41.6
Combined Sets
41.7
Creating a First Report
41.8
Data Labels
41.9
Create Folders
41.10
Sorting Data
41.11
Add Totals, Subtotals, and Grand Totals to Report
41.12
Hands-on: Install Tableau Desktop
41.13
Hands-on: Connect Tableau to various Datasets: Excel and CSV files
Learn Tableau Charts
34
42.1
Area Chart
42.2
Bar Chart
42.3
Box Plot
42.4
Bubble Chart
42.5
Bump Chart
42.6
Bullet Graph
42.7
Circle Views
42.8
Dual Combination Chart
42.9
Dual Lines Chart
42.10
Funnel Chart
42.11
Traditional Funnel Charts
42.12
Gantt Chart
42.13
Grouped Bar or Side by Side Bars Chart
42.14
Heatmap
42.15
Highlight Table
42.16
Histogram
42.17
Cumulative Histogram
42.18
Line Chart
42.19
Lollipop Chart
42.20
Pareto Chart
42.21
Pie Chart
42.22
Scatter Plot
42.23
Stacked Bar Chart
42.24
Text Label
42.25
Tree Map
42.26
Word Cloud
42.27
Waterfall Chart
42.28
Hands-on: Create and use Static Sets
42.29
Hands-on: Create and use Dynamic Sets
42.30
Hands-on: Combine Sets into more Sets
42.31
Hands-on: Use Sets as filters
42.32
Hands-on: Create Sets via Formulas
42.33
Hands-on: Control Sets with Parameters
42.34
Hands-on: Control Reference Lines with Parameters
Learn Tableau Advanced Reports
22
43.1
Dual Axis Reports
43.2
Blended Axis
43.3
Individual Axis
43.4
Add Reference Lines
43.5
Reference Bands
43.6
Reference Distributions
43.7
Basic Maps
43.8
Symbol Map
43.9
Use Google Maps
43.10
Mapbox Maps as a Background Map
43.11
WMS Server Map as a Background Map
43.12
Hands-on: Create Barcharts
43.13
Hands-on: Create Area Charts
43.14
Hands-on: Create Maps
43.15
Hands-on: Create Interactive Dashboards
43.16
Hands-on: Create Storylines
43.17
Hands-on: Understand Types of Joins and how they work
43.18
Hands-on: Work with Data Blending in Tableau
43.19
Hands-on: Create Table Calculations
43.20
Hands-on: Work with Parameters
43.21
Hands-on: Create Dual Axis Charts
43.22
Hands-on: Create Calculated Fields
Learn Tableau Calculations & Filters
17
44.1
Calculated Fields
44.2
Basic Approach to Calculate Rank
44.3
Advanced Approach to Calculate Ra
44.4
Calculating Running Total
44.5
Filters Introduction
44.6
Quick Filters
44.7
Filters on Dimensions
44.8
Conditional Filters
44.9
Top and Bottom Filters
44.10
Filters on Measures
44.11
Context Filters
44.12
Slicing Filters
44.13
Data Source Filters
44.14
Extract Filters
44.15
Hands-on: Creating Data Extracts in Tableau
44.16
Hands-on: Understand Aggregation, Granularity, and Level of Detail
44.17
Hands-on: Adding Filters and Quick Filters
Learn Tableau Dashboards
6
45.1
Create a Dashboard
45.2
Format Dashboard Layout
45.3
Create a Device Preview of a Dashboard
45.4
Create Filters on the Dashboard
45.5
Dashboard Objects
45.6
Create a Story
Server
7
46.1
Tableau online
46.2
Overview of Tableau
46.3
Publishing Tableau objects and scheduling/subscription
46.4
Hands-on: Create Data Hierarchies
46.5
Hands-on: Adding Actions to Dashboards (filters & highlighting)
46.6
Hands-on: Assigning Geographical Roles to Data Elements
46.7
Hands-on: Advanced-Data Preparation
Excel
0
Excel: Basics to Advanced
82
48.1
Excel tutorial
48.2
Text to Columns
48.3
Concatenate
48.4
The Concatenate Function
48.5
The Right Function with Concatenation
48.6
Absolute Cell References
48.7
Data Validation
48.8
Time and Date Calculations
48.9
Conditional Formatting
48.10
Exploring Styles and Clearing Formatting
48.11
Using Conditional Formatting to Hide Cells
48.12
Using the IF Function
48.13
Changing the “Value if false” Condition to Text
48.14
Pivot Tables
48.15
Creating a Pivot Table
48.16
Specifying PivotTable Data
48.17
Changing a PivotTables Calculation
48.18
Filtering and Sorting a PivotTable
48.19
Creating a PivotChart
48.20
Grouping Items
48.21
Updating a PivotTable
48.22
Formatting a PivotTable
48.23
Using Slicers
48.24
Charts
48.25
Creating a Simple Chart
48.26
Charting Non-Adjacent Cells
48.27
Creating a Chart Using the Chart Wizard
48.28
Modifying Charts
48.29
Moving an Embedded Chart
48.30
Sizing an Embedded Chart
48.31
Changing the Chart Type
48.32
Chart Types
48.33
Changing the Way Data is Displayed
48.34
Moving the Legend
48.35
Formatting Charts
48.36
Adding Chart Items
48.37
Formatting All Text
48.38
Formatting and Aligning Numbers
48.39
Formatting the Plot Area
48.40
Formatting Data Markers
48.41
Pie Charts
48.42
Creating a Pie Chart
48.43
Moving the Pie Chart to its Own Sheet
48.44
Adding Data Labels
48.45
Exploding a Slice of a Pie Chart
48.46
Data Analysis − Overview
48.47
types of Data Analysis
48.48
Data Analysis Process
48.49
Working with Range Names
48.50
Copying Name using Formula Autocomplete
48.51
Range Name Syntax Rules
48.52
Creating Range Names
48.53
Creating Names for Constants
48.54
Managing Names
48.55
Scope of a Name
48.56
Editing Names
48.57
Applying Names
48.58
Using Names in a Formula
48.59
Viewing Names in a Workbook
48.60
Copying Formulas with Names
48.61
Difference between Tables and Ranges
48.62
Create Table
48.63
Table Name
48.64
Managing Names in a Table
48.65
Table Headers replacing Column Letters
48.66
Propagation of a Formula in a Table
48.67
Resize Table
48.68
Remove Duplicates
48.69
Convert to Range
48.70
Table Style Options
48.71
Table Styles
48.72
Cleaning Data with Text Functions
48.73
Removing Unwanted Characters from Text
48.74
Extracting Data Values from Text
48.75
Formatting Data with Text Functions
48.76
Date Formats
48.77
Conditional Formatting
48.78
Sorting
48.79
Filtering
48.80
Lookup Functions
48.81
Pivoting
48.82
Hands-On
Statistical Analysis
0
Introduction to Statistical Analysis
15
50.1
Introduction to Probability
50.2
Probability Addition Rule
50.3
Probability Multiplication Rule
50.4
Distributions
50.5
Correlation
50.6
Regression
50.7
Hypothesis Testing
50.8
ANOVA and Chi-Square Tests
50.9
Data Cleaning
50.10
Imputation Techniques
50.11
Measure Of central tendency, Measures of Dispersion
50.12
Graphical Techniques, Skewness & Kurtosis, Box Plot
50.13
Descriptive Stats
50.14
Central Limit Theorem, Confidence interval
50.15
Hands-On
Introduction to Data Analytics
10
51.1
Data Analytics Overview
51.2
Importance of Data Analytics
51.3
Types of Data Analytics
51.4
Descriptive Analytics
51.5
Diagnostic Analytics
51.6
Predictive Analytics
51.7
Prescriptive Analytics
51.8
Benefits of Data Analytics
51.9
Data Visualization for Decision Making
51.10
Hands-On
SQL
13
52.1
Fundamentals of SQL Syntax and Statements
52.2
Data Filtering and Sorting Techniques
52.3
Utilization of Single-Row Functions
52.4
Aggregate and Group Functions
52.5
Analytical (Window) Functions
52.6
Data Definition Language (DDL) – Creating and Managing Schema Objects
52.7
Data Manipulation Language (DML) – Inserting, Updating, and Deleting Data
52.8
Implementation of Data Constraints
52.9
Set Operations – UNION, INTERSECT, and MINUS
52.10
Subqueries and Nested Queries
52.11
Joins and Multi-Table Queries
52.12
Transaction Control Language (TCL) – COMMIT, ROLLBACK, SAVEPOINT
52.13
Data Control Language (DCL) – GRANT and REVOKE Permission
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