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Whether you are just starting or an experienced pro, Codegnan Python for Data Science course enables you to master Data Science Analytics using Python. You will work on various Python libraries like SciPy, NumPy, Matplotlib, Lambda function, etc. You will master data science analytics skills through real-world projects in multiple domains like Big Data, Data Science and Machine Learning. Our hands-on approach will help you reach your goals faster with more confidence. Let us help you master the concepts of Data Science with Python based on real-life industry cases to increase your job market value.

Python for Data Science Certification Overview

Python Training for Data Science by Codegnan will help you gain in-depth knowledge of designing, developing, and deploying data science applications to open up the shortest career path to become a data scientist as it is among the highest paid and most in-demand professions. The training will enable you to master Python 3.8+ along with the concepts like statistical methods, data acquisition and analysis, Machine Learning algorithms, predictive analytics, etc. At the end of the course, you will work on a capstone project to check your learning skills. Take your step ahead in the amazing and fruitful career in data science!

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Python for Data Science Certification Curriculum

Python Introduction and setting up environment
• Introduction to programming
• R or Python ?
• Why Python for Data Science?
• Different job roles with Python
• Different Python IDEs
• Downloading and setting up python environment
Hands-on: Installing Python - IDLE

● Python Basic Syntax and Data Types
• Python input and output operations.
• Comments
• Variables, rules for naming variables
• Basic Data Types in Python
• Typecasting in python
Hands-on: Using comments,variables,data types and type casting in python program

● Operators in Python
• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Identity operators
• Membership Operators
• Bitwise Operators
Hands-on: Working with different data types in a program

● Strings in Python
• Creating strings
• String formatting
• Indexing
• Slicing
• String methods
Hands-on: Performing string operations

● Lists
• Creating lists
• Properties of lists
• List indexing
• List slicing
• List of lists
• List Methods
• Adding,Updating & removing elements from lists
Hands-on: Slicing, Indexing and using methods on lists

● Tuples
• Syntax to create tuples
• Tuple properties
• Indexing on tuples
• Slicing on tuples
• Tuple methods
Hands-on: Working with tuples

● Sets
• Syntax for creating sets
• Updating sets
• Set operations and methods
• Difference between sets, lists and tuples
Hands-on: Performing set operations in a program

● Dictionaries
• Syntax for creating Dictionaries
• Storing data in dictionaries
• Dictionaries keys and values
• Accessing the elements of dictionaries
• Dictionary methods
Hands-on: Creating dictionaries and using dictionaries methods

● Python conditional statements
• Setting logic with conditional statements
• If statements
• If -else statements
• If-elif-else statements
Hands-on: Setting logic in programs using conditional statements

● Loops in Python
• Iterating with python loops
• while loop
• for loop
• range
• break
• continue
• pass
• enumerate
• zip
• assert
Hands-on: Iterating with loops in python

● List and Dictionaries comprehension
• Why List comprehension
• Syntax for list comprehension
• Syntax for dict comprehension
Hands-on: Using List and Dictionary comprehension

● Functions
• What are Functions
• Modularity and code reusability
• Creating functions
• Calling functions
• Passing Arguments
• Positional Arguments
• Keyword Arguments
• Variable length arguments (*args)
• Variable Keyword length arguments (**kargs)
• Return keyword in python
• Passing function as argument
• Passing function in return
• Global and local variables
• Recursion
Hands-on: Creating our own functions,passing arguments and performing operations

● Anonymous Function
• Lambda
• Lambda with filter
• Lambda with map
• Lambda with reduce
Hands-on: Working with lambda, filter,map and reduce in python

● Generators
• Creating and using generators
Hands-on: Creating and using generators
● Modules
• Creating modules
• Importing functions from different module
• Importing Variables from different modules
• Python builtin modules
Hands-on: Creating and importing Modules

● Packages
• Creating packages
• Importing modules from package
• Different ways of importing modules and packages
Hands-on: Creating and importing packages

● Exceptions and Error handling
• Syntax errors
• Logical errors
• Handling errors using try,except and finally
Hands-on: Handling Errors with try and except

● Classes and Objects (OOPS)
• Creating classes & Objects
• Attributes and methods
• Understanding __init__ constructor method
• Class and instance attributes
• Different types of of methods
• Instance methods
• Class methods
• Static methods
• Inheritance
• Creating child and parent class
• Overriding parent methods
• The super() function
• Understanding Types of inheritance
• Single inheritance
• Multiple inheritance
• Multilevel inheritance
• Polymorphism
• Operator overloading
Hands-on: Creating classes,objects. Creating methods and attributes. Working with different methods. Using inheritance and polymorphism.

● Date and Time
• date module
• time module
• datetime module
• time delta
• formatting date and time
• strftime()
• striptime()
Hands-on: working with date and time

● Regex
• Understanding the use of regex
• re.compile()
• re.find()
• re.split()
• re.sub()
• Meta characters and their use
Hands-on: using regular expression to search patterns

● Files
• Opening file
• Opening different file types
• Read, write, close files
• Opening files in different modes
Hands-on: Reading, Writing, Appending, opening, and closing files.

● Web Scraping
• Installing BeautifulSoup
• Understanding web structures
• Chrome devtools
• request
• Scraping data from web using beautifulsoup
• scraping static websites
• Selenium
• Scraping dynamic websites using selenium
Hands-on: Scraping static and dynamic websites using beautifulsoup and selenium

● Database Access
• Accessing Database using sqlite3 and MySql
• Creating tables
• Insert Values
• Commit changes
• Query
• Update and Delete
Hands-on: Connecting and Querying database

• Introduction to databases.
• Why SQL?
• Execution of an SQL statement.
• Installing MySQL
• Load data.
• Use, Describe, Show table.
• Select.
• Limit, Offset.
• Order By.
• Distinct.
• Where, Comparison Operators, NULL.
• Logic Operators.
• Aggregate Functions: COUNT, MIN, MAX, AVG, SUM.
• Group By.
• Having.
• Order of Keywords.
• Join and Natural Join.
• Inner, Left, Right, and Outer Joins.
• Sub Queries/Nested Queries/Inner Queries.
• Data Control Language: GRANT, REVOKE

• Introduction
• What is NoSql Database
• What NoSql Database can do
• Categories of NoSql Databases
• Downloading and installing MongoDB
• Creating Database and storing Data
• Nesting
• Retrieving
• Querying
• Defining views
• Reduce function to reduce data
Hands on - Storing and Analysing Scraped Dataset Using SQL and NoSql

Descriptive Statistics

• Data- types of data
• Measure of central tendency - Mean-Median-Mode
• Measure of shape - Variance- Standard deviation, Range, IQR
• Measure of shape - Skewness and kurtosis
• Covariance
• Correlation - Pearson correlation & Spearman's rank correlation
• Probability - Events,Sample Space,Mutually exclusive events, Mutually exclusive events
• Classical and Conditional Probability
• Probability distribution - Discrete and Continuous
• Uniform Distribution
• Expected values, Variance and means
• Gaussian/Normal Distribution
• Properties, mean, variance , empirical rule of normal distribution
• Standard normal distribution and Z-score

Inferential Statistics
• Central Limit Theorem
• Hypothesis testing - Null and Alternate hypothesis
• Type - I and Type - II error
• Critical value, significance level, p-value
• One tailed and two tailed test
• T-test - one sample, two sample and paired t-test
• f-test
• One way and two way ANOVA
• Chi-Square test

NumPy and Pandas For Data Analysis and Machine Learning

• Introduction to numpy
• Advantages of Numpy over lists
• Creating Numpy arrays - 1-D,2-D,N-D arrays
• Checking the attributes - shape,size,dimensions,dtype
• NumPy - Indexing and slicing
• Numpy arithmetic
• Numpy broadcasting
• Linear Algebra using numpy
• Numpy universal functions
• Reshaping numpy arrays

Pandas for Data Analysis
• Creating Dataframe
• Checking Attributes
• Reading different file types
• Basic Essential functionality
• Indexing and Selecting data
• iloc and loc functionality
• Working with missing data
• Grouping, Reshaping and Selecting Data
• Using Aggregate for Data Statistics and description
• Merge, Concat and join dataframes
• Pivot tables, Crosstab, Stack and Unstack
• Working with categorical data
• Working with time series
• Working with text data
• Writing/Saving files
• Basic Plotting using pandas

Data Visualization Using Matplotlib, Seaborn And Plotly

• Line plot
• Setting Labels, Titles, xticks and yticks
• Subplots and figure size
• Multiple Line Plots, adding legend
• Bar charts - What are they , When to use it
• Bar chart for comparing categorical data
• Horizontal bar chart, Stacked Bar charts and Multiple barcharts
• Histogram to check the distribution of numerical data
• Bins in histogram
• Histogram to check the shape of data
• Scatterplot
• 3D-Scatterplot
• Checking relation b/w two variables using scatterplot
• Multivariate Analysis using scatterplot
• Adding Colorbar
• Boxplot - 5 number summary
• Checking spread using boxplot
• Comparing values using pie chart
• Area Plot
• Changing the style of graphs, adding grid

• Plotting statistical graphs using seaborn
• Advantages over matplotlib
• Basic Line Plots
• Count Plots
• Adding hue
• Barplots - Horizontal and vertical barplots
• Distplot - Checking distribution of data
• Histplot - for plotting histograms
• Boxplots in seaborn
• Multiple boxplots
• Scatterplot
• Pairplot
• Regression plots
• Jointplot
• Violin Plot
• Jitter plot

Data Analysis Using Numpy, Pandas and Matplotlib - HR Analytics

Plotly and Cufflinks
• Loading Plotly and Cufflinks
• Loading the Data
• Quick visualization with custom bar charts
• Interactive Bubble charts
• Interactive Animations and Facet plots
• Represent Geographic Data as an Animated graph

Hands on - Analysing Gapminder dataset

Data Analysis And Visualization Projects


• What are dimensions?
• Why is high dimensionality a problem ?
• Introduction to MNIST dataset with (784 Dimensions)
• Into to Dimensionality reduction techniques
• PCA (Principal Component Analysis) for dimensionality reduction
• t-sne (t-distributed stochastic neighbor embedding

Hands-on: Applying Dimensionality Reduction on MNIST data

• What is machine learning?
• How Machine Learning works?
• Applications of machine learning
• Different types of machine learning
• How do we know machines are learning right?
• Different stages of machine learning projects.

Data Transformation and Preprocessing
• Handling Numeric Features
• Feature Scaling
• Standardization and Normalization
• Handling Categorical Features
• One Hot Encoding, pandas get_dummies
• Label Encoding
• More on different encoding techniques

Train,Test and Validation Split
• Simple Train and Test Split
• Drawbacks of train and test split
• K-fold cross validation
• Time based splitting

Overfitting And Underfitting
• What is overfitting ?
• What causes overfitting?
• What is Underfitting ?
• What causes underfitting ?
• What are bias and Variance ?
• How to overcome overfitting and underfitting problems ?

• Introduction to Linear Regression
• Understanding How Linear Regression Works
• Maths behind Linear Regression
• Ordinary Least Square
• Gradient Descent
• R - square
• Adjusted R-square
• Polynomial Regression
• Multiple Regression
• Performance Measures - MSE, RMSE, MAE
• Assumption of Linear Regression
• Ridge and Lasso regression
• RFE (Recursive Feature elimination)

Hands On - Problem formulation and Case Study on Hotstar, Netflix, And housing prices Dataset


Logistic regression

• Introduction to classification problems
• Introduction to logistic regression
• Why the name regression ?
• The sigmoid function
• Log odds
• Cost function
• Feature importance and model interpretability
• Collinearity of features
• Feature engineering for non-linearly separable data

Performance Metrics for Classification Algorithms

• Accuracy Score
• Confusion Matrix
• Precision - Recall
• F1-Score
• ROC Curve and AUC
• Log Loss

Hands On - Real World Case Study on IBM HR Employee Attrition dataset

K Nearest Neighbors

• Introduction to KNN
• Effectiveness of KNN
• Distance Metrics
• Accuracy of KNN
• Effect of outlier on KNN
• Finding the k Value
• KNN on regression
• Where not to use KNN
Hands On - Different case study on KNN

Natural Language Processing

• Introduction to NLP
• Converting Text to vector
• Data Cleaning
• Preprocessing Text Data - Stop word removal, Stemming , Tokenization, Lemmatization
• Collecting Data from the web
• Developing a Classifier
• Building Pipelines for NLP projects
• Uni-grams,bi-grams and n-grams
• tf-idf
• Word2Vec
Hands On - Text Summarization, WebScraping for data, Sentiment Analysis, Topic Modelling, Text Summarization and Text Generation

Naive Bayes

• Refresher on conditional Probability
• Bayes Theorem
• Examples on Bayes theorem
• Exercise problems on Naive Bayes
• Naive Bayes Algorithm
• Assumptions of Naive Bayes Algorithm
• Laplace Smoothing
• Naive Bayes for Multiclass classification
• Handling numeric features using Naive Bayes
• Measuring performance of Naive Bayes
Hands On - Working on spam detection and Amazon Food Review dataset

Support Vector Machines

• Introduction to SVM
• What are hyperplanes ?
• Geometric intuition
• Maths behind svm’
• Loss Function
• Kernel trick
• Polynomial kernel, rbf and linear kernels
• SVM Regression
• Tuning the parameter
• GridSearch and RandomizedSearch
• SVM Regression
Hands On - Case Study SVM on Social network ADs and Gender recognition from voice dataset

Decision Tree
• Introduction to Decision Tree
• Homogeneity and Entropy
• Gini Index
• Information Gain
• Advantages of Decision Tree
• Preventing Overfitting
• Advantages And Disadvantages
• Plotting Decision Trees
• Plotting feature importance
• Regression using Decision Trees

Hands On - Decision Tree on US Adult income dataset

Ensemble Learning
• Introduction to Ensemble Learning
• Bagging (Bootstrap Aggregation)
• Constructing random forests
• Runtime
• Case study on Bagging
• Tuning hyperparameters of random forest(GridSearch, RandomizedSearch)
• Measuring model performance
• Boosting
• Gradient Boosting
• Adaboost and XGBoost
• Case study on boosting trees
• Hyperparameter tuning
• Evaluating performance
• Stacking Models
Hands On - Talking Data Ad Tracking Fraud Detection case study

Time Series

• Time Series Basic
• Time Series Data Loading and Visualization
• Featuring Engineering on time series data
• Resampling techniques on Time Series Data
• Time Series Transformation
• Power Transformation
• Moving Averages
• Exponential Smoothing
• White Noise
• Random Walk
• Decomposing Time Series
• Differencing
• Splitting time series data
• Naive (Persistence) Model
• Auto Regression Model (AR)
• Moving Average Model (MA)
• Stationary time series
• Linear Regression and Model Creation

Hands On - Working with stock and Pollution dataset


• Introduction to unsupervised learning
• Applications of Unsupervised Learning
• Kmeans Geometric intuition
• Maths Behind Kmeans
• Kmeans in presence of outliers
• Kmeans random initialization problem
• Kmeans++
• Determining the right k
• Evaluation metrics for Kmeans
• Case study on Kmeans
• Hierarchical Clustering
• Agglomerative and Divisive
• Denodgrams
• Case study on hierarchical clustering
• Segmentation
• Case Study on Segmentation
• DBSCAN - Density based clustering
• MinPts and Eps
• Core Border and Noise Points
• Advantages and Limitation of DBSCAN
• Case Study on DBSCAN clustering

Hands On - Applying Unsupervised models on Retail data and mall customer dataset


• Introduction to Recommender Systems
• Recommender Engine Architecture
• Content Based Recommendation
• Cosine Similarity
• K-Nearest Neighbors and content Recommendation
• Neighborhood based collaborative filtering
• User-based collaborative filtering
• Item Based Collaborative filtering
• Tuning Collaborative filtering Algorithms
• Matrix Factorization methods - PCA, SVD, NMF
• Train/Test cross-validation
• Evaluation metrics for recommender systems

Hands On - Building A Recommender System on movie lens dataset


• Introduction
• History of Deep Learning
• Perceptrons
• Multi-Level Perceptrons
• Representations
• Training Neural Networks
• Activation Functions

Artificial Neural Networks

• Introduction
• Deep Learning
• Understanding Human Brain
• In Depth Perceptrons
• Example for perceptron
• Multi Classifier
• Neural Networks
• Input Layer
• Output Layer
• Sigmoid Function
• Introduction to Tensorflow and Keras
• CPU vs GPU
• Introduction to Google collaboratory
• Training Neural Network
• Understanding Notations
• Activation Functions
• Hyperparameter tuning in keras
• Feed Forward Networks
• Online offline mode
• Bidirectional RNN
• Understanding Dimensions
• Back Propagation
• Loss function
• Regularization
• Training for batches

Hands On - Working with MNIST and Fashion MNIST dataset Using Tensorflow and Keras API

• Introduction to CNN
• Applications of CNN
• Idea behind CNN
• Understanding Images
• Understanding Videos
• Convolutions
• Striding and Padding
• Max Pooling
• Edges, Gradients and Textures
• Understanding Channels
• Formulas
• Weight and Bias
• Feature Map
• Pooling
• Combining
• Intro to OpenCV
• Reading and Writing Images
• Saving images
• Draw shapes using OpenCV
• Face detection and eye detection using OpenCV
• CNN with keras
Hands On - Working with ImageNet Dataset

CNN - Transfer Learning

• Introduction
• AlexNet
• GoogleNet
• ResNet
• Transfer learning using keras
Hands On - Case Study on the models and Working with Cat vs Dog dataset

RNN - Recurrent Neural Networks

• Introduction to RNNs
• Training RNNs
• RNN Formula
• Architecture
• Batch Data
• Simplified Notations
• Types of RNNs
• GRUs
• Training RNN
• One to many
• Vanishing Gradient problem

Hands On - Working on sentiment analysis and text generation


Python for Data Science Certification Projects

Project 1

Market Analysis

Data analysis is the process of collecting and organizing data in order to draw helpful conclusions from it. The process of data analysis uses analytical and logical reasoning to gain information from the data. In this project, we have a dataset has the data regarding goods sales, mode of transport, product details

Project 2

Credit Card Fraud Detection with User Interface

Finance fraud is a growing problem with far consequences in the financial industry and while many techniques have been discovered. As credit card transactions become a widespread mode of payment focus has been given to recent computational methodologies to handle the credit card fraud problem. We are going to analyze all the important features regarding credit card transactions and train the best model to analyze the data.

Project 1

Market Analysis

Data analysis is the process of collecting and organizing data in order to draw helpful conclusions from it. The process of data analysis uses analytical and logical reasoning to gain information from the data. In this project, we have a dataset has the data regarding goods sales, mode of transport, product details

Project 2

Credit Card Fraud Detection with User Interface

Finance fraud is a growing problem with far consequences in the financial industry and while many techniques have been discovered. As credit card transactions become a widespread mode of payment focus has been given to recent computational methodologies to handle the credit card fraud problem. We are going to analyze all the important features regarding credit card transactions and train the best model to analyze the data.

Certification In This Program

MTA Certification - Python for Data Science

Python for Data Science Certification

Training on different technologies provided by Codegnan is a set of blended learning models that brings classroom learning experience with its world-class LMS. We understand the effort of students; thus, as a token of motivation, our training is honored by top leading industries like Microsoft and HP. After the successful completion of your Python certification course, you will be awarded Microsoft MTA certification.

Can I get a job with Python for Data Science certification?

Python has emerged as the standard language and has been called the "Next Big Thing" and a "Must" for Professionals. It is quite impressive that 41 top organizations in the world have adopted Python as their primary programming language in a very small span of time. Some of the big players like Quora, Facebook, YouTube, SlideShare, Dropbox, Pinterest, Reddit, and Netflix have most of their new code written in Python.

It is an open secret in the developer world that Google has now adopted Python as its secondary coding language, and has committed to using it more in its new product offerings. With Python, you will have an open door for various career prospects like Software Engineer Python Developer Research Analyst Data Analyst Data Scientist Software Developer.

MTA Certification - Python for Data Science

Python for Data Science Certification Trainer/s



He is a tech-expert with 7 years of industrial experience in Python, Data Analysis, Big Data, Machine Learning and NLP. He has 360 degrees of expertise in all these subjects. He is known for his practical approach to different real-time industrial problems. He is known for his great interest in helping students reach their true potential and scale greater heights. Believing in Problem-based teaching pedagogies, he left his job in Malaysia as a data engineer and came back to the newly born state to fill the void between students and the industry.



A Master in Computational Intelligence and also Data Science Consultant for Andhra Pradesh State Skill Development Corporation(APSSDC) blended with a passion for nurturing the meaning of education with technology. With expertise in Machine Learning, Data Analytics, Natural Language Processing, and Cloud Computing, he believes in teaching all functionalities to the core making his life's motto to train students to be a Data Scientist rather than a Data Engineer. With a vision of building a better society using technological innovation, he joined Codegnan to bring out and enrich the essence of Technical Education with industrial excellence. His ambition is to make Codegnan the Centre of Excellence towards LeadIndia 2020.

Python for Data Science Certification FAQs

Following aspirants can opt for Python Certification Course: Data Scientists, Programming Enthusiasts, Project Managers Software developers, Software engineers, Technical leads Architects

There are no specific prerequisites to learn Python programming. A person with less or no programming knowledge can start learning Python.

As per the quick growth of data stored previously or currently utilized by industries, Python is also growing like a rapid-fire. Because of its small codes, Python has been successfully contributing to analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries. The growth of Python can be successfully seen in industries like Machine Learning, Artificial Intelligence, Natural Language Processing & Text processing, Data Science, Big Data. Networking, and so on.

After the successful completion of your Python Certification Course, you will be a Pythonist in a real manner, MTA certification is simply a cherry on the cake, and a quick job is what you need the most.

Python programming language training's main objective for the student is to develop a foundation of programming skills up to the higher end to solve the different programming logic. A student will be able to write a different type of logic at the end of the sessions. After learning a python programming language course, the student is able to build web applications, database access, and data analysis. After completing the Python training, a student will be able to attend any MNC Company interview and can solve the technical rounds both theoretically and practically. A student is able to build web pages and host in cloud servers which can be accessed by anyone in the world.

Python is the best programming language for processing the data in various stages. Integration with any programming language. You can design a project frontend with PHP and integrate backend with python. Advanced technologies like NLP, AI, and Machine Learning are built using python. It has a collection of extensive support libraries The topmost web applications like BitTorrent, Spotify, Reddit, Bitbucket, Yahoo Maps, Youtube, etc. are built using Python Easy to learn programming language Education at CodeGnan will assure you the ability to code application by the end of the Python training.

You will get 24*7 support and lifetime access to the LMS where course material like presentations, installation guides & class recordings are available.


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