Best Data Science Training in Jodhpur (Classroom Course With Job)

Best Data Science Course in Jodhpur With Placement (Classroom Training)

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Give wings to your career by learning data science with WsCube Tech. The number of jobs requiring data science skills is increasing every year in India and globally. In the coming 5 years, it will become the #1 most-demanded profession.

Now is the best time to learn this skill and prepare yourself for a bright future. Enroll now in the best Data Science Training in Jodhpur to learn from industry experts, work on live projects, get certified, and find assistance in placement.

Best Data Science Course in Jodhpur With Placement (Classroom Training)

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Instructor-Led Data Science Training in Jodhpur

WsCube Tech offers the most comprehensive, practical-oriented, and the best data science course in Jodhpur. Whether you are a beginner or have some prior experience, this course is the right fit for you as our expert trainers cover everything from scratch.

Starting with Python programming and data analysis, and going deep down to machine learning, deep learning, and artificial intelligence (AI), you will learn all the concepts in detail. It’s 6-month data science training in Jodhpur with certificate and placement assistance.

During this classroom course, you will master data science skills, including data collection, extraction, integration, data mining, statistical analysis, predictive analysis, and numerous essential concepts. By the end of the training in data science, you will be on your way to kickstarting a thriving career as a successful Data Scientist.

Trusted by 28,000+ candidates all over India for both classroom and online data science course, we believe in helping our learners build a great career. Plenty of our students are working with top organizations like TCS, Infosys, E&Y, Accenture, IBM, and VMWare.

It’s time for you to choose the best data science course and start mastering advanced skills with WsCube Tech!

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Data Science Course Syllabus

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

Why WsCube Tech is Best Data Science Training Institute in Jodhpur?

Training by Data Science Experts

Training by Data Science Experts

To ensure that you get high-quality content & learning experience, we provide the entire Data Science training in Jodhpur by industry experts having more than a decade of experience.

Regular Assessments

Regular Assessments

Your level of knowledge, learning, and practicals are regularly assessed by the trainer so that you can work on your weak areas and improve your skills further.

Rigorous Classroom Training

Rigorous Classroom Training

This is a classroom Data Science course in Jodhpur. For the best learning experience, the classroom is fully-digitized, distraction-free, and offers 1:1 interaction with the trainer.

100% Practical-Oriented

100% Practical-Oriented

Data Science is something that requires immense practice. On that front, you will get to work on real projects in Python, machine learning, AI, etc. for practical exposure and experience.

Placement Assistance

Placement Assistance

On course completion, we assist you in Data Science interview preparation and resume building. Your interviews are arranged with top companies hiring data scientists in India.

Data Scientist Certification

Data Scientist Certification

Along with advanced skills, you get a professional data science certificate. You can add it to your resume and explore great job opportunities in India or globally.

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“It's time for you to future-proof your career!”

“We know that we are influencing the foundations of your future, and we take this responsibility very seriously. With WsCube Tech, I ensure that you always get top-class training backed by practical projects and future prospects. Wishing you a successful & future-proof career!”

Kushagra Bhatia, Founder, WsCube Tech

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Data Science Course Jodhpur 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.

Classroom-based or online data science certification course by WsCube Tech is the best option for you to learn data science skills and build a good career.

The data science course fee in Jodhpur is INR 35,000.

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

Yes. Along with training, we also offer summer training and internship in data science.

The duration of the data science course in Jodhpur is 6 months.

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.

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 for their teams.

There are dedicated data analytics companies providing services to other organizations. By doing a 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.

For those wondering how much does a data scientist earn in India, here is the answer:

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.

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.

Top Brands 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 The Best Data Science Certification Training in Jodhpur 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

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WsCube Tech is the leading IT training institute and software development company in India. With headquarters in Jodhpur, it is on a mission to skill, reskill, and upskill individuals in the ever-evolving technology domains.

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