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"Data Science Course"

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Interested in starting a career in a high-demand field? Sign up for India's top online/offline data science course to get started.

In this course, you'll have the opportunity to learn data science online with guidance from experienced industry experts. The course is divided into basic and advanced modules and spans six months. You'll delve into topics like Python programming, Machine Learning, Deep Learning, and Data Analytics. Additionally, we provide support to help you find a job and advance your career.

 Data Science Course

Batch Details

Duration Timings
Batch - 1 9:00 AM to 11:00 AM
Batch - 2 11:00 AM to 01:00 PM
Batch - 3 01:00 PM to 03:00 PM
Batch - 4 03:00 PM to 05:00 PM
Batch - 5 05:00 PM to 07:00 PM

Course Fees

₹24,950/ -

(including GST)

Quick Overview of Data Science Certification Course by Arfi Technology

In today's digital age, companies are using technology to grow faster. Data is valuable because it gives important information that helps businesses grow. The need for skilled data scientists has gone up in India and around the world in recent years.

Nowadays, there are lots of new tools and technologies, but there aren't enough data scientists. Many companies really need data scientists. This is a great opportunity for you to learn and become a talented data scientist and analyst.

Arfi Technology provides India's premier data science course, designed to equip you with the skills to analyze data effectively. With guidance from industry experts, you'll tackle substantial datasets during this 6-month training, preparing you for a promising career as a data scientist with excellent earning potential.

Arfi Technology certificate

What will you discover in this Data Science?

Our curriculum is meticulously crafted, offering a comprehensive learning experience aligned with the latest industry trends and standards!

  • Introduction to Python and its features
  • Installing Anaconda /Jupyter
  • Variables Data Type and Object
  • Difference between Compiler and Interpreter
  • Basic Data Types of Python
  • Comments in Python
  • Operators
  • Types of Operators
  • Print method and its argument
  • Different print formatting
  • Input method
  • Typecasting

  • Conditional Statements
  • If elif and else statements
  • Nested if
  • Exercise for if else condition
  • Loops
  • For loop and range function
  • While loop
  • Break and continue statements
  • Nested loops in Python
  • For-else and while-else statement
  • Exercise: Conditional and loop-based questions

  • Introduction to List
  • Indexing on List
  • Slicing on List
  • List Methods I– Append, Extend, Insert
  • List Methods II– Pop, Remove, Clear
  • List Methods III– Sort()
  • List Methods IV– Reverse
  • List Methods V–Count, Index
  • Using Condition statement in list
  • Using Loops in list
  • Exercise for list and assignment

  • Introduction to Tuples
  • Tuple Methods– Index, count
  • Tuple Exercises

  • What is Dictionary
  • Dictionary Methods I– Clear, copy, Fromkeys
  • Dictionary Methods II– get, update,
  • Dictionary Methods III– Pop, popitem, setdefault
  • Dictionary Methods IV– key, values, items
  • Dictionary Methods V–setdefaults

  • Strings
  • Indexing on Strings
  • Slicing on Strings
  • Immutable Strings
  • String Methods I– upper, lower, title, capitalize, swapcase
  • Strings Methods II– strip, find, index, isalnum
  • Strings Methods III– startswith, endswith, split, replace
  • Strings Methods IV– isalpha, isalnum, isupper, islower
  • Using Condition statement with string
  • Using Loops with string
  • Exercises

  • What is Set?
  • Set Methods I– add, copy
  • Set Methods II– difference, difference update, symmetric difference, symmetric_difference_update
  • Set Methods III– union, intersection, intersection_Update
  • Set Methods IV– isdisjoint, issubset, isuperset,
  • Set Methods V–pop, clear, remove

  • Different types of functions
  • User-defined functions
  • Creating functions with and without arguments
  • Positional and default arguments
  • Return and Non-Return type function in Python
  • Recursive functions
  • Unpacker Object in Python
  • *args and **kwargs function in python
  • Scope of variables - local and global
  • Anonymous Functions– lambda
  • Exercise– Functions, and Recursion

  • Importing modules
  • Using modules I– math, random
  • Using modules II– itertools, collections
  • Inbuilt Functions I– map, reduce, filter
  • Inbuilt Functions II– enumerate, eval, zip,
  • Exercise– Inbuilt functions and libraries

  • Working with files
  • Opening and closing a file
  • Modes of opening a file
  • Reading, writing, and appending to a file
  • Handling text files using readlines, read, tell, seek methods
  • Handling CSV files in Python

  • What is an Exception?
  • Understanding try-except-else block of code
  • Types of exceptions I– ZeroDivisionError, TypeError, NameError
  • Types of exceptions II– ValueError, IndexError
  • Handling multiple Exceptions
  • Raise keyword to generate exceptions

  • Understanding class and objects
  • Self keyword
  • Creating a class in Python
  • Understanding constructor
  • Difference between a constructor and a method
  • Types of variable– Instance and static
  • Creating, accessing, modifying, and deleting Instance variables
  • Creating, accessing, modifying, and deleting Static variables
  • Types of Methods - Instance, Class, and Static methods
  • Getter and Setter methods
  • Understanding Inheritance
  • super method
  • Types of Inheritance - single, multilevel, multiple, hierarchical
  • Polymorphism and method overriding
  • Encapsulation
  • Exercise – OOPC

  • Overview of Artificial Intelligence (AI), Machine Learning, and Data Science
  • Understanding the varied applications of Data Science
  • Different sectors using Data Science

  • Understanding Statistics
  • Understanding data, sample, and population
  • Types of data– Qualitative and Quantitative
  • Descriptive Statistics
  • Uni-variate Data Analysis– Measure of Central Tendency
  • Mean, Median and Mode
  • Uni-variate Data Analysis– Measure of Dispersion
  • Range, Variance, Standard Deviation
  • Bi-variate Data Analysis– Covariance and Correlation
  • Inferential Statistics
  • Central Limit Theorem
  • Random Variable and different types of random variable
  • Probability Distribution Functions
  • Normal Distribution
  • Binomial and Poisson Distributions
  • Skewness and different types of skewness
  • What is Hypothesis Testing?
  • Null and Alternate Hypothesis
  • P-value, Level of significance
  • Confidence Level and Confidence Interval
  • One Sample Z-test
  • learner’s T-test
  • Chi Square Test
  • Exercise– Statistics

  • Introduction to NumPy
  • Features of NumPy
  • Create NumPy Array
  • Different ways to create NumPy array
  • Numpy Custom Array Creation using zeros, ones, linspace, etc.
  • NumPy Array Indexing
  • NumPy 1D, 2D, and 3D Indexing
  • NumPy slicing
  • NumPy advanced indexing and slicing
  • Generating NumPy arrays with random values
  • NumPy Array Broadcasting
  • NumPy Array Iterating
  • NumPy Array Manipulation
  • NumPy Arithmetic Operation
  • NumPy Statistical Function
  • numpy.amin() and numpy.amax()
  • numpy.ptp(), numpy.percentile()
  • numpy.median(), numpy.mean()
  • numpy.average(), Standard Deviation
  • Variance
  • NumPy Random
  • What is Random Number
  • Generate Random Number
  • Generate Random Float
  • Generate Random Array
  • Generate Random Number From Array
  • Random Data Distribution
  • What is Data Distribution?
  • Random Distribution
  • Random Permutations
  • Random Permutations of Elements
  • Shuffling Arrays
  • Generating Permutation of Arrays
  • Seaborn
  • Visualize Distributions With Seaborn
  • Distplots
  • Import Matplotlib
  • Import Seaborn
  • Plotting a Distplot
  • Plotting a Distplot Without the Histogram
  • Normal (Gaussian) Distribution
  • Normal Distribution
  • Visualization of Normal Distribution
  • Binomial Distribution
  • Visualization of Binomial Distribution
  • Difference Between Normal and Binomial Distribution
  • Poisson Distribution
  • Visualization of Poisson Distribution
  • Difference Between Normal and Poisson Distribution
  • Difference Between Poisson and Binomial Distribution
  • Uniform Distribution
  • Visualization of Uniform Distribution
  • Logistic Distribution
  • Visualization of Logistic Distribution
  • Difference Between Logistic and Normal Distribution
  • Multinomial Distribution
  • Exponential Distribution
  • Visualization of Exponential Distribution
  • Relation Between Poisson and Exponential Distribution
  • Chi Square Distribution
  • Visualization of Chi Square Distribution
  • Rayleigh Distribution
  • Visualization of Rayleigh Distribution
  • Similarity Between Rayleigh and Chi Square Distribution
  • Pareto Distribution
  • Visualization of Pareto Distribution
  • Zipf Distribution
  • Visualization of Zipf Distribution

  • Introduction to Pandas
  • Understanding Series in Pandas
  • Creating Series using– NumPy array, list, tuple, from a .csv/excel file
  • Series methods– mean, sum, count, etc.
  • Series indexing and slicing using– iloc and loc
  • Reading a .csv, .excel files using Pandas– read_csv, read_excel
  • Understanding DataFrame in Pandas
  • Creating DataFrame using NumPy array, list, tuple, from a .csv/excel file
  • Head, tail, and sample methods for DataFrame
  • DataFrame indexing and slicing using– iloc and loc
  • Accessing column values from a DataFrame
  • Set DataFrame index, sort index, and values
  • DataFrame query
  • Find unique values for a column in DataFrame
  • Group by method
  • Data wrangling methods I– merge, append, concat
  • Data wrangling methods II– map, apply, applymap
  • Data cleansing I– rename columns, rearrange columns
  • Data cleansing II– remove null values, fill null values
  • Data cleansing III– drop rows, drop columns
  • Handling datetime in Pandas
  • Pivot table

  • Introduction to Matplotlib visualization
  • Bar Chart
  • Line Chart
  • Scatter Chart
  • Pie Chart
  • Histogram
  • Boxplot
  • Subplots
  • Exercise– Matplotlib and Pandas

  • Introduction to Seaborn visualization
  • Countplot
  • Boxplot
  • Violinplot
  • Pairplot
  • Heatmap
  • Scatterplot
  • Plotting Geospatial maps using Plotly

  • Exploratory Data Analysis Overview
  • Project– EDA On Cardio Good Fitness Data
  • Project– Bank dataset EDA
  • Project– Used cars dataset EDA

  • Introduction to Machine Learning
  • Understanding different types of Learning– Supervised and Unsupervised Learning
  • Understanding Supervised and Unsupervised algorithms
  • Difference between Supervised and Unsupervised Learning

  • Splitting data into training and test datasets
  • Understanding the working and equation of Regression Analysis
  • Regression metrics– R2-score, MAE, MSE, RMSE
  • Implementation of Simple Linear Regression
  • Implementation of Multiple Linear Regression
  • Project– Heating and Cooling Load Prediction

  • Understanding Confusion Matrix
  • Understanding the concept of True positive, False Positive, True
  • Negative and False Negative
  • Classification Metrics– Accuracy, Precision, Recall, F1-Score
  • Bias Variance, Underfitting, and Overfitting

  • Understanding the working of Logistic Regression
  • Derivation of Sigmoid function
  • Implementation of Logistic Regression
  • Project– Diabetic patient Classification

  • Understanding the working of KNN
  • Algorithm of KNN
  • Implementation of KNN
  • Project– Social Network Ads Classification

  • Understanding the working of Decision Tree
  • Understanding Gini and Entropy criterion
  • Implementation of Decision Tree Classification
  • Understanding the working of Random Forest Classification
  • Concept of Bootstrapping
  • Implementation of Random Forest Classification
  • Project– Iris Flower Classification
  • Project– Placement Prediction

  • Understanding the working of Naive Bayes
  • Implementation of Naive Bayes Classification
  • Project– News Classification

  • Understanding the working of K-Means Clustering
  • Understanding of Elbow method to find the optimal number of clusters
  • Implementation of K-Means Clustering
  • Project– Shopping dataset Clustering

  • Understanding the working of PCA
  • Understanding Eigen values and Eigen vectors
  • Implementation of PCA

  • Difference between Bagging and Boosting
  • Understanding working of AdaBoost
  • Implementation of AdaBoost
  • Understanding working of XGBoost
  • Implementation of XGBoost

  • Introduction to NLP
  • Removing Stop Words, Stemming, Lemmatization
  • Count Vectorizer and Tf-Idf
  • Project– Spam vs. ham Email Classification

  • Reading and displaying an image using OpenCV
  • Image Transformation operations
  • Filtering and Thresholding
  • Erosion and Dilation
  • Object Detection using Haar Cascade Files– Face and car Detection
  • Project– Clustering colors in images

  • Introduction to Neural Network
  • What is a Neuron?
  • Working of a Neuron
  • Perceptron Model
  • Concept of Hidden layers and Weights
  • Concept of Activation Functions, Optimizers, and Loss Functions
  • Equation of a General Neural Network
  • Understanding Backpropagation

  • Introduction to TensorFlow
  • Importing TensorFlow
  • Using TensorFlow on Colab
  • What is a tensor?
  • Indexing and Slicing
  • Tensorflow basic operations

  • Understanding different Activation Functions
  • Linear, Sigmoid, Tanh, Relu
  • Understanding different Loss Functions
  • MSE, Binary CrossEntropy, etc.
  • Understanding different Optimizers
  • Gradient Descent, Adam, etc.

  • Implementation of a Neural Network
  • Implementation of ANN for Regression
  • Implementation of ANN for Classification
  • Project– Customer Churn Modelling

  • Understanding CNN (Convolutional Neural Network)
  • Understanding the Convolution process
  • Concept of Filter, strides
  • Pooling Layer
  • Fully Connected Layer
  • Project– MNIST Image Classification
  • Project– Fashion MNIST Image Classification

  • MNIST Image Classification
  • Fashion MNIST Image Classification
  • Customer Churn Modelling
  • Spam vs Ham Email Classification
  • HR Analytics Classification
  • Big mart Sales Prediction
  • Bank Loan Prediction

What Sets Arfi Technology Apart in Data Science Course?

Help system.

Learn From Experts

A data science and machine learning expert with over 11 years of experience teaches the entire course. You'll learn everything thoroughly and in a way that's easy to

Skilled Mentorship

Dedicated Doubt-Clearing Sessions

We understand that learners can have doubts related to any topic. To help you learn effectively, we arrange dedicated doubt-clearing sessions for you.

Placement Assistance

Live Training Classes

It’s an online/offline Data Science Course with live classes. You can interact with the mentor, and get personalised recommendations.

Engage in Real Project Work.

Hands-on Projects

Another reason that makes it the best online course on data science is that we offer practical exposure to learners. You get to work on real and live projects during the training.

Career support is readily available

100% Job Assistance

It is a data science online course with placement, which means after the training and projects, we help you in getting hired at good

Certification in Web Development Available

Data Science Certification.

Along with the right skills, you also get certified by India’s leading IT India’s leading company. This is a data science online/offline course with certificate.

Arfi Technology owner Aqmal Arfi

"Secure your future with a career that's built to last!"

"We understand the profound impact we have on shaping your future, and we embrace this responsibility wholeheartedly. At Arfi Technology, we are committed to providing you with premier training, enriched by practical projects, to ensure your career is both successful and future-proof. Best wishes for your journey!"

Aqmal Arfi, Founder, Arfi Technology Pvt Ltd

Here’s what our amazing clients are saying

This is a clean and concise way to showcase their happy customers, and help prospects gain the assurance they need before investing.

Data Science Course FAQs

Data science is the field that brings together statistics, scientific methods, data analysis, as well as machine learning (ML), and artificial intelligence (AI). The purpose of data science is to find value from heaps of data from websites, customers, smartphones, sensors, software, etc.

In the field of data science, the typical job title is "data scientist." A data scientist's main task is to use their skills to examine data, clean it up, group it together, and make changes to it. This analysis of data helps businesses make decisions based on facts and discover new patterns they didn't know about before.

The primary subjects covered in the data science and analytics courses are Python, Machine Learning, Deep Learning, Data Analytics, and Artificial Intelligence (AI).

While it is not necessary to have professional knowledge of programming or technical stack, but if you have some basic knowledge, it is an add-on and helps you learn data science fast.

Becoming a data scientist involves learning specific skills and subjects like Python, data analysis, machine learning, and deep learning. To begin, you should take a good data scientist course. Afterward, you can find a job with top companies in the country.

A data scientist's job is to gather lots of data and use smart analysis techniques. With the right skills, you can apply your analysis to solve important problems for businesses, customers, and others.

Since it is still one of the unexplored IT fields in India, many people wonder:

  • Is there a demand for data scientists in India?
  • Is it hard to get a data science job in India?

Data science is a really hot job right now, both in the country and abroad. Many companies, from small startups to big ones, are searching for talented data scientists to join their teams.

You can take a course in data science with Python and practice analyzing data to open doors to job opportunities at companies that specialize in data analytics for other businesses.

A few of the top companies hiring data scientists include LensKart, Microsoft, Accenture, Oracle, Pinterest, Slack, Intel, Uber, Ernst & Young (EY), IBM, Aditya Birla Group, etc.

You can enroll in our online content writing course and gain the right skills to start your career without any prior experience.

The average data scientist salary in India is INR 7.00 LPA. A fresher’s salary starts at INR 5 LPA, whereas someone with 1-4 years of experience can make INR 6 to 10 LPA. Data scientists with 5+ years of experience make more than 11 LPA.

Yes. You will get the data science certificate on course completion.

No need for concerns. You'll have access to recorded sessions of both online and offline classes for review or in case you miss a class. Plus, you can engage with your mentor during future classes to address any queries or doubts.

Companies such as Microsoft, IBM, Uber, Intel, Accenture, and Oracle are recruiting data scientists in India and offering an average annual salary of 7 lakh rupees.

>It’s time for you to acquire the right skill set and grab the opportunities.

Enroll now in the best Data Science course in Jaunpur! !

  • Python Basic to Advanced
  • Object-oriented Programming
  • Data Analytics With Statistics
  • NumPy and Pandas
  • Seaborn and Plotly
  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • TensorFlow
  • Neural Networks
  • And a total of 35+ modules