Manal Softech: India’s Most-Trusted IT Training Institute

Duration	Timings
(Mon - Sat) 5 Months	8:00 AM to 9:00 AM
(Mon - Sat) 5 Months	6:00 PM to 7:00 PM
(Mon - Sat) 5 Months	7:00 PM to 8:00 PM
(Mon - Sat) 5 Months	8:00 PM to 9:00 PM

What You’ll Learn in This Data Science Online Training?

Well-structured & comprehensive curriculum designed according to latest trends and industry 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
Student’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

Data Science Full 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.

The job profile in data science is usually a data scientist. A data scientist’s work is to utilize his skills for analyzing data, cleansing, aggregating, and manipulating it. This data analysis helps in making data-driven business decisions and uncovering unknown patterns.

In India, Ujjain is currently offering one of the best data science courses online. As a leading institute in the field of IT since 2011, it has trained thousands of students, job seekers, and professionals in various fields. You get training from industry experts with regular live classes on Zoom, dedicated doubt-clearing sessions, professional certificate, as well as guaranteed placement assistance.

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.

Yes. Definitely. This is a skill that doesn’t force you to join an offline batch. You can do data science online training in India from a reliable institute like Ujjain and become a data scientist.

In order to become a data scientist, you must have the right skills and command of several subjects and technologies. These include Python, data analytics, machine learning, deep learning, etc. To start with, you must enroll in the best data scientist certification course. Then you can get placement or get hired by top companies in the country.

The job role of a data scientist is to collect large amounts of data and analyze it intelligently. With the right skills, you can implement your analytics to solve critical challenges for businesses, customers, and other problems.

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?

The answer is that it is one of the top careers in the country and abroad today. Skilled data scientists are in high demand. Startups to SMBs to large organizations are looking for qualified candidates in their teams.

There are dedicated data analytics companies providing services to other organizations. By doing data science with Python course and practicing analysis of data, you can grab these opportunities.

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.

Not to worry. In case you miss a live class, you will get the recording of the class, which you can watch according to your time. For any doubts, you can ask the trainer in the next class or the doubt-clearing session.

The fees of our data science certification course is INR 25,000 (including 18% GST).

Yes. On completion of the course, we will prepare you for the interview. Following preparation, we will align your interviews with several top companies in the country and help you get placed.

Companies like Microsoft, IBM, Uber, Intel, Accenture, and Oracle are hiring data scientists in India at an average salary of INR 7 LPA.

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

Enroll in India’s Best Data Science Certification Training Today!

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

Learners Love Ujjain’s Data Science Certification Course

We are proud to have positively influenced the career foundations for thousands of learners across India and Asian countries.

Simply awesome experience! This is one of the best online data science courses in India. The approach was good right from the beginning till the end. The trainer was way better than I had expected and helped me clear all the important concepts. Thanks a lot!

Jaideep Shinde

Great content, training, and instructor! All the modules of the course are covered in detail with regular assignments and practice. I highly recommend it to anyone who wants to build a career in data science in India or abroad. Without any doubt, this is one of the best data science courses in Hindi.

Kaushik Bardiya

The best thing about this course is that it has the most detailed and comprehensive content. Many topics that are ignored by others are included here. Moreover, a person with any level of skills can go for it as it starts with Python and goes deeper into machine learning, data analytics, and deep learning. Kudos Ujjain!

Sneha Pandey

There are many online data science courses with certificate, but this is the best. Ujjain delivers what it commits. Regular live classes, experienced trainer, doubt-clearing sessions, regular assignments and practice, and quality content. Great job.