The document outlines a comprehensive training program focused on digital skills, data science, machine learning, and related technologies offered by Digicrome for the period 2022-2023. It includes detailed course content covering topics such as Python programming, advanced Python libraries, statistics, deep learning, natural language processing, and data visualization tools like Power BI and Tableau. Registration details and contact information for potential participants are also provided.

1. Period 2022 - 2023
Open For Registration!
DS & AI
ADMISSION
Since our inception, we have been
focused on developing cutting-edge
learning methodologies by involving
learners and experienced faculty, along
with providing individuals and
corporations with high-quality training
materials that aids professionals in
accomplishing their career objectives
and furthering their careers.
We work with some of the world's finest
institutions and certifying authorities
and we aspire to provide high-quality
training to professionals all across the
globe. We have a proven track record of
effectively training thousands of
professionals in both classroom and
online training. Come join us and let us
transform your professional lives via
digital skills.
ABOUT US
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www.digicrome.com
The Best program
Kickstart Your Career With New Skills
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Information
01203113765
Phone
Website
Mail
www.digicrome.com
Address
C-20, SUITE NO 2,FIRST
FLOOR .SECTOR 2 NOIDA
info@digicrome.com

2. Anaconda
About Anaconda
Why use Anaconda?
Anaconda Installation.
How to install?
Using Anaconda
Environment Creating in Anaconda
Activating Environment in
Anaconda
Deleting Environment
Libraries Installing In Anaconda
and more
IDE: Jupyter Notebook Introduction
What is Jupyter Notebook
Why use JN
Layout understanding of Jupyter
Jupyter Notebook Shortcuts and
how to work with it.
Markdown cell best use
Working and creating project folder
and launching notebook in it
Launching Jupyter from Anaconda
prompt
Navigator
Add deleting cells
Markdown cell Tricks
How to work fast in jupyter
Python Introduction
History of python
Why python is the first choice for AI
why work on python why it is
considered easy
Python Core
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Set
Unique values in set
What is set
Sub Set
Set functions
Functions of set
Dictionary
Dict()
Key and value
Item,Value and Key function
Other ways of initializing a Dict
Other functions of Dict()
Dict Comprehension
Operations on List
Slice
Negative Indexing
Slice with negative indexes
Slicing in String
Mutation in List
Non Mutable strings
The del statement
Conditional Statement
if Statements
else Statements
elif Statements
break and continue Statements
pass Statements
If condition in single line
Variable allocation with if condition
Loops Statements
Range Function
Range
Range with parameters
Range with negative index
For Loop
For loop with a string
For loop with range function
For for printing patterns
While Loop
While loop with counter
While loop with If Cond.
While Loop with IF Condition for string
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Coding on the Python IDE Jupyter
(integrated development
environment)
Types of values (int,float,str,bool)
Argument Passing and what are
variable
Variable creation
Type method
String Operation
Index
Negative Index
Slicing
Negative Slicing
Reversing and step in string
Immutable Strings
Length method
Len of string
Len of List
Print
Sep in print
End in print
Normal print with variable
Dot format in print
Format by f
Input and Output
Using str in input
Type change of input
Eval,int,float,str use in input
Can we use variable in print just
like print
Data Structure in Python
List
List function
List Indexing
List with list() function
List Comprehension
More on list
Tuple
Index and Count function in tuple
List vs Tuple
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3. Functions
Defining Functions
Functions with inputs
Functions with arguments
Functions with return
Calling functions for multiple jobs
Default Argument Values
Arguments passing
Positional-or-Keyword Arguments
* arg and ** kwarg
Lambda Expressions
Mapping
Filter
Combining all Map Filter Lambda for
complex problems
Comprehensions
List Comprehensions
Nested List Comprehensions
Comprehensions with Dictionary
Comprehensions with if the condition
File Handling
Reading and Writing Files
Errors and Exceptions
Syntax Errors
Exceptions
Handling Exceptions
Raising Exceptions
Exception Chaining
Classes
What is class
What is Objects
Class Definition Syntax
Class Objects Creation
Inheritance
Multiple Inheritance
Other Oops Concept
Iterators
Generators
Generator Expressions
Projects and some other topics
TEST for each part
Assignments
Exercise Question 100+
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Advance Python Libraries
Numpy
Functions in Numpy
What is array
All core methods of numpy
Matrix
Matrix Operation
Types of Plots
Bar Plot
Scatter Plot
Box Plot
Line Plot
Hex Plot
Stack Plot
Area Plot
Other Types of Plot
Pandas
Pandas methods
Null Handeling in pandas
Encoding in pandas
Plotting in pandas
Stats in pandas
Data reading and data saving in pandas
Dropping and feature engineering
through pandas
Other major works in pandas
Matplotlib
Plotting in matplotlib.pyplot
Axis in plot
Title and header in the plot
Legend and label in the plot
Types of plotting in matplot
Subplot
Controlling plots
More on pots
Seaborn
Why learn Seaborn
Count Plot
Heatmap Plot
Other plots
Advantage of seaborn plot
More and all about seaborn
TEST for each part
Assignments
Exercise Question
Advance Python
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Stats - Part of Data Science
Stats roll in Data Science
Population and sample
Types of variables
Central tendency
Coefficient of variance
Standard Deviation
Variance
Covariance
Pearson Correlation
Spearman correlation
Skewness and Kurtosis
Inferential statistics
Normal distribution
Mean mode median
Test hypotheses
Null Hypotheses
Alternate Hypotheses
Correlation matrix
Central limit theorem
Confidence interval
T-test
Type I and II errors
ANOVA
Range
Binomial Distribution
Black-Scholes model
Boxplots
Chebyshev's Theorem
Chi-squared Distribution
Chi-Squared table
Cohen's kappa coefficient
Combination
Combination with replacement
Comparing plots
Continuous Uniform Distribution
STATISTICS
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4. Cumulative Frequency
Co-efficient of Variation
Correlation Co-efficient
Cumulative plots
Poisson Distribution
Frequency Distribution
Histograms
Kurtosis
Normal Distribution
Pie Chart
Poisson Distribution
Probability
Probability Additive Theorem
Probability Multiplicative Theorem
Probability Bayes Theorem
Probability Density Function
Residual analysis
Residual sum of squares
Root Mean Square
Scatterplots
Skewness
Standard Deviation
Type I & II Error
Z-Score
MinMax Schaller
TEST for each part
Assignments
Exercise Question
Stats II Part
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Machine Learning Core
Supervised
Linear Regression
L1 - Lasso
L2 - Rigid
Logistic Regression
SVM
KNN
Naive Bayes
Decision Tree
Unsupervised
KNN
Hierarchical
Mean shift
DBScan
Ensemble
Bagging and Boosting
Bagging VS Bosting
Bagging
RandomForest
Voting Classifier
Boosting
AdaBoost
Gradient Boosting
XGBoost Frame Work
The technique’s in Machine Learning
PCA
Dimension Reduction
Scaling
Z-score
Standardization
Min Max Scaler
Normalization
Normalization General Method
Feature engineering
Model Selection
DATA SCIENCE
AND
MACHINE LEARNING
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What are NULL
Handling NULL
Handling Categorical Values
Encoding
What are Encoding Techniques
Encoding for Nominal and Ordinal Data
Encoding by Pandas
Encoding by Other Lib
Encoding by Mapping and core Python
Accuracy Score
R2 Score
Adjusted VS R2 Score
R2 VS Adjusted R2
What is Hyper Tuning
Cross-Validation
Grid Search CV
Random Search CV
TEST for each part
Assignments
Exercise Question
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5. What is Time Series?
Regression vs Time Series
Examples of Time Series data
Trend, Seasonality, Noise and Stationarity
Detrending
Successive Differences
Auto-correlation
Moving Average
Moving Average and Smoothing of data
checking Stationarity
The exponentially weighted forecasting
model
Lagging
Correlation and Auto-correlation
Holt Winters Methods
Single Exponential smoothing
Holt’s linear trend method
Holt’s Winter seasonal method
ARIMA and SARIMA
TEST for each part
Assignments
Exercise Question
Time Series Forecasting
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Introduction of Deep Learning
What is AI
Subdomain of AI
Working of each subdomain
Core Language for all domains and why
python is the best fit
Deep Learning Introduction
Limitations of Machine Learning
Neural Network
Proton and neurons
Advantage of Deep Learning over
Machine learning
Regression in NN
Classification in NN
Clustering and Autoencoding in NN
Underfitting
Overfitting
Regulization and Drop out
Optimization
Understanding Neural Networks with
TensorFlow
How Deep Learning Works?
Input processing in NN
Activation Functions
Illustrate Perceptron
Training a Perceptron
Important Parameters of Perceptron
What is TensorFlow?
Basic model in NN
Constants, Placeholders, Variables
Model Creation
Neural Networks with TensorFlow
Introduction to Single Perceptron
Concept of Neural Networks
Introduction of Multi-Layer Perceptron
Frontpropagation and
Backpropagation
Concept of Backpropagation
Multi-layer Classifier using TensorFlow
Multi-Layer Regression using
TensorFlow.
Deep Learning
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Deep Neural Nets
Introduction to Deep Networks its
application
How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
Convolutional Neural Networks (CNN)
Introduction to CNNs
Architecture of a CNN
Convolution and Pooling layers in a
CNN
Understanding and Visualizing a CNN
Building a convolutional neural network
for image classification.
Recurrent Neural Networks (RNN)
Introduction to RNN Model n.
Sequences Modelling
Training RNNs with Backpropagation
Long Short-Term Memory (LSTM)
Bidirectional LSTM
Recurrent Neural Network Model
TEST for each part
Assignments
Exercise Question
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6. NLP Introduction
NLP - About nltk lib
Word Analysis
Key Word Detection
Stop Words
Lemitization
Stemming
Lem vs Stem
Syntactic Analysis
Semantic Analysis
NLP - Part of Speech (PoS) Tagging
Applications of NLP
Python and nltk
What is NLP (Natural Language Processing)"
understanding NLP
Syntax and Semantics
Part Of Speech Tagging
Hidden Markov Models
Uni-Gram and N-Gram Taggers
Evaluating Tagger Model
Basic Neural Network
RNN and NLP collaboration logic
Introduction to perception
NN propagation
Pattern Recognition and Machine Learning
Semantics Analysis:
Lexical semantics and word-sense
disambiguation
Compositional Semantics
Semantic Role Labeling
Semantic Parsing
Information Extraction:
Named entity recognition and relax extraction
IE using sequence labelling
Machine Translation:
Basic issues in MT
Statistical translation
Word alignment
Phrase-based translation
Summarizer and its working
Sentimental Analysis working
Chat Processing
Nltk Chatbot
Language modelling
Text Modeling
Word Modeling
Other kinds of modelling
TEST for each part
Assignments
Exercise Question
Natural Language Processing
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Introduction of Power BI
ABOUT Power BI
Advantage of Power BI Power BI
Installation Steps
The layout of Power BI tool
Data Sources
Data Modeling
Dashboard
Visualization
Excel Integration
Working and Sharing Dashboards
DAX
DAX Basics in Power BI
What is DAX Functions
DAX Functions types
Formal DAX Functions Introduction
DAX Parameter and their Naming
Conventions
DAX for Aggregation
DAX for Filter
DAX for Time Intelligence
DAX for Date and Time
DAX for Information
DAX for Logical
DAX for Math & Trigonometric
DAX for Text
TEST for each part
Assignments
Exercise Question
POWER BI
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The Introduction to Google Data Studio GDS
The layout of Data Studio
Data Studio frame overview
Edit and View
Share and multiple teams
Add Data
Add Chart
Types of charts
Add a control
URL Embed
Image
Adding text
Lines and types of arrows
Shapes
Theam and Layout
View
Insert
Page
Arrange
Resource and Help
Data and Style
Add a filter
Add a parameter
Google Data Studio working
Access controls
Main Home Page
Data source working
Report overview working
Report edit mode
Build your report
Connecting the Data
Add and configure report controls for a
filtered view
Format and Design Reports
Data visualization
Create and use report templates
Creating Amazing Dash Boards on GDS
Project from scratch
TEST for each part
Assignments
Exercise Question
GOOGLE DATA STUDIO
Google Data Studio
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7. Tableau Introduction
Start Page
Show Me
Connecting to Excel Files
Connecting to Text Files
Connect to Microsoft SQL Server
Connecting to Microsoft Analysis Services
Creating and Removing Hierarchies
Bins
Joining Tables
Data Blending
Learn Tableau Basic Reports
Parameters
Grouping Example 1
Grouping Example 2
Edit Groups
Set
Combined Sets
Creating a First Report
Data Labels
Create Folders
Sorting Data
Add Totals, Sub Totals and Grand Totals to
Report
Learn Tableau Charts
Area Chart
Bar Chart
Box Plot
Bubble Chart
Bump Chart
Bullet Graph
Circle Views
Dual Combination Chart
Dual Lines Chart
Funnel Chart
Traditional Funnel Charts
Gantt Chart
Grouped Bar or Side by Side Bars Chart
Heatmap
Highlight Table
Histogram
Cumulative Histogram
Line Chart
TABLEAU
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Lollipop Chart
Pareto Chart
Pie Chart
Scatter Plot
Stacked Bar Chart
Text Label
Tree Map
Word Cloud
Waterfall Chart
Geographic map
Filled map
Crosstab
Combines axis
Motion chart
Reference lines
Learn Tableau Calculations & Filters
Calculated Fields
Basic Approach to Calculate Rank
Advanced Approach to Calculate Rank
Calculating Running Total
Filters Introduction
Quick Filters
Filters on Dimensions
Conditional Filters
Top and Bottom Filters
Filters on Measures
Context Filters
Slicing Filters
Data Source Filters
Extract Filters
Learn Tableau Dashboards
Create a Dashboard
Format Dashboard Layout
Create a Device Preview of a Dashboard
Create Filters on Dashboard
Dashboard Objects
Create a Story
TEST for each part
Assignments
Exercise Question
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Contact us
0120-3113-765
www.digicrome.com