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Data Science Training Course in Vijayawada

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Our experience in data science will make you confident in constructing the base of data science and make you capable of working on real-life problems that make effective use of data technology. You'll gain information on topics like TensorFlow, Develop Chatbots, R for Data Science, Spark MLlIb, and Python for Data Science with CodeGnan's Data Science training. Not only this, getting yourself Data Science certified with prominent badges like MTA will be a great deal! So, join the force of Data Scientists and enjoy the top-paying job as many countries are already getting a shortage of it. The training will teach you everything you need to know to become a data scientist at a fraction of the cost of traditional programs.

Data Science Training in Vijayawada Overview

Data is treated as the new currency in the world. Every day there are more than 2.5 quintillion bytes of data generated which needs to be sorted and analyzed to be used later. The volume of data is growing exponentially and it results in a vast demand for data scientists. Data Scientists are the detectives of the big data world, responsible for uncovering useful data by processing large datasets. And the study of data analysis spans the whole data life cycle, just like an investigator is accountable for discovering evidence, discovering them, and eventually making their case in court.

Data Science training is helping individuals take advantage of this vast demand. Companies are spending anywhere between hundreds of thousands of dollars to billions of dollars on software and personnel to be able to analyze the available data to get an advantage over their competitors as well as to increase their market share. Learn to think, work, and earn like a Scientist - a Data Scientist! What are you waiting for now? Every day is a missed opportunity!

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Data Science Training in Vijayawada Curriculum

• Introduction to Python.
• Who is using Python today?
• Installation and setting up the environment.
• Basic syntax.
• Built in data types.

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


Data Science Training in Vijayawada Projects

Project 1

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 2

Predictive Modelling for Cricket Data Analysis

Predictive Modelling for Cricket Data Analysis
Cricket prediction is comparatively difficult as there are many factors that can influence the result or outcome of the cricket match. Earlier basic prediction systems for cricket match consider only the venue and disregard the factors like weather, stadium size, captaincy etc., The factors like the venue of the match, pitch, weather conditions first batting, or fielding all play a vital role in predicting the winner of the match. Thus we are going to predict the winning team based on some key factors by taking T20 Cricket Dataset from the previous years and will come to prediction based on the statistical data provided.

Project 1

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 2

Predictive Modelling for Cricket Data Analysis

Predictive Modelling for Cricket Data Analysis
Cricket prediction is comparatively difficult as there are many factors that can influence the result or outcome of the cricket match. Earlier basic prediction systems for cricket match consider only the venue and disregard the factors like weather, stadium size, captaincy etc., The factors like the venue of the match, pitch, weather conditions first batting, or fielding all play a vital role in predicting the winner of the match. Thus we are going to predict the winning team based on some key factors by taking T20 Cricket Dataset from the previous years and will come to prediction based on the statistical data provided.

Certification In This Program

Codegnan Certification for Data Science

Data Science Certification Training in Vijayawada

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 online Data Science course in Vijayawada, you will be awarded Codegnan’s certification.

Can I get a job with Data Science certification?

Data Science is growing very quickly and it is now counted in the top future trends of technology. Becoming Data Science certified is securing a good job in Data Science and recognizing yourself unique in the market. Also with Data Science, you open many doors for your high-paying career. With Data Science, you can become a Data Analyst, Data Scientist, Data Journalist, Machine Learning Scientist, Artificial Intelligence expert, Software Engineer, Business intelligence Professional, Machine Learning Engineer or much more.

Codegnan Certification for Data Science

Data Science Training in Vijayawada 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.

Data Science Training in Vijayawada FAQs

Although you are recommended to not miss any of your Python course class, but, if in case you miss any class then you will be provided with the video of previously recorded sessions.

We’ll be providing an approx 10% discount on the courses. You can check discounts and other offers from our student counselors. Kindly request for the call back by filling our contact us form or simply drop us an email. We’ll get back to you within 24 hours of the span.

We will assist you in case of any queries, even after the completion of your Data Science online training. You are always welcome to reach through our customer care number or email us your query. We would love to assist you.

We will be helping you get your dream job in Data Science by sharing your resume with various recruiters and assisting you with building your resume and preparing for the interviews.

In offline or classroom training, you will be given dedicated time and tools when you can implement the programs you have learned during the training.

Our trainers are industry experts who have completed Masters in AI and have work experience of 10+ years in the industry.

If you want to enroll yourself in the Data Science training program, then payments can be made using any of the following options, and a receipt of the same will be issued to you right after the payment confirmation.

1. UPI
2. Visa Debit/Credit Card
3. American Express and Diners Club Card
4. Master Card
5. PayPal

The future of Data is very bright. The scope of Data Science has led to successful ventures and developments in the fields like software development, science, arts, business, education and government administration.

In the Data Science certification course, you will get Microsoft Technology Certification and Codegnan certification.

Data Science allows you different career paths e.g., Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist, etc.

As a fresher, Data Science Developer’s salary is $30,000 to $50,000 per year.

On average, a Data Science Developer’s salary is $100,634 to $1,30,000 per year.


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