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Best Data Science Course in Jaipur (Become Successful Data Scientist)

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Sign up for our data science course in Jaipur and learn from experts from the comfort of your home. WsCube Tech is known for offering quality online education programs and a seamless learning experience. Our data science training include free demo and doubt sessions, group discussions, and one-on-one interaction with mentors.

After every module, you will work on real-world projects that will hone your skills in cutting-edge technologies, such as machine learning, R, Python, Spark, Tableau, and Hadoop.

Be a part of our top data science institute in Jaipur to improve your skills and be ready to step into the industry with confidence.

Best Data Science Course in Jaipur

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About Our Data Scientist Course in Jaipur

Look around; data affects almost every decision and every aspect of running a business, from the price of goods to marketing campaigns. Our online data science course in Jaipur will help you understand how raw information is processed in the tech world to make critical decisions.

With our extensive online courses, you will build expertise in data visualisation, manipulation, machine learning, and predictive analytics to kickstart a successful career in no time. Moreover, you will get constant support from mentors, acquire new skills, and gain insights to reach your career goals in the data science domain.

Being a trusted data science institute in Jaipur, we assist you in becoming career ready to take on the challenges of the industry. Whether you are a tech proficient or have zero experience in the IT industry, our courses are customised to meet the needs of every learner.

Rest assured that you will soon be a master in transforming data using advanced tools and contributing to business decision-making. The live classes of our data scientist course in Jaipur enable you to be a part of interactive tutorials and learning sessions. Also, our mentors have created easy-to-understand study material so you can learn at your pace and explore the field practically.

Whether you are a business owner or want to work for a big company, our data science courses in Jaipur are perfect for understanding how data plays a vital role in making sense of raw information.

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Curriculum of Our Data Science Course Jaipur

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
  • 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

Why Choose WsCube Tech for Data Science Training in Jaipur?

Learn from the Best

Learn from the Best

We have the most qualified and skilled educators who have more than a decade of experience in the field. Make the best of their expertise and insights in live classes.

Access to the Latest Tools

Access to the Latest Tools

WsCube Tech allows all learners free access to cutting-edge tools. During the data science course in Jaipur, you use them to work on real-world projects for better understanding.

Hands-on Experience

Hands-on Experience

After completing each module, you will work on projects that will help you gain practical experience, discover new technologies, and get industry-ready.

Dedicated Doubt Sessions

Dedicated Doubt Sessions

We conduct live doubt sessions for all the learners where they can ask queries and questions related to different topics, industries, and job opportunities.

Free Demo Classes

Free Demo Classes

Before you enroll in our data science course in Jaipur and pay the fee, you can join demo classes to be assured of the quality of training and the unparalleled expertise of mentors.

Live Learning Sessions

Live Learning Sessions

WsCube Tech believes in interactive classes where learners and mentors come together for group discussions and exchange their thoughts in live classes.

Wscube Tech owner Kushagra bhatia

“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

What learners Say About Our Data Scientist Course in Jaipur!

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



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Data Science Course Jaipur FAQs

Data science is a multidisciplinary industry that requires extracting valuable information from extensive data. With advanced methods, such as deep learning, visualisation, and machine learning, it allows to generate meaningful insights and interpret the patterns for better understanding.

Data science offers a wide range of career options with different tasks based on the applicant's skills and expertise.

Once you complete our data science courses in Jaipur, you can apply for various job roles, such as:

  • Data Architect
  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Machine Learning Engineer
  • Business Intelligence Analyst.

A data scientist is a professional who collects, organises, analyses, and visualises big data and draws meaningful conclusions. They also communicate the insights to the business leaders to help them make informed decisions.

Also, they are skilled at gathering information from different sources, processing it into a suitable format, and uploading it into the analytics system for deeper analysis. They must be adept at computer programming, artificial intelligence, data mining, and predictive analytics. Using machine learning algorithms, they transform raw data into actionable information.

Considering the evolution and changes in the data science industry, you need to prepare yourself for the following expected trends:

  • Cloud migration
  • Automated machine learning (AutoML)
  • Predictive analytics
  • Augmented consumer interface
  • Cloud-native solution
  • AI-as-a-service

The average salary of a proficient and knowledgeable data scientist starts from ₹8,00,000 per year. After a few years of experience and more expertise, you can make around ₹30,00,000 or more annually.

As a leading data science institute in Jaipur, we offer the most extensive course to our learners that cover the three major areas- machine learning, statistics, and data mining. All these areas are essential for any professional to succeed in the field and work as a foundation for manipulating and analysing data.

Our modules include the following topics:

  • Machine Learning and Artificial Intelligence
  • Database Management using SQL
  • Business Analytics
  • Time Series Forecasting
  • Statistics and Mathematics
  • Exploratory Data Analysis
  • Programming using Python or R
  • Data Mining
  • Data Visualization using Tableau or Power BI

You don’t need any specific academic background or professional experience to sign up for our data scientist course in Jaipur. If you are interested to learn about the field and can commit to it, then we welcome you to be a part of our amazing community.

Looking at the current trend in the tech industry and the crucial role that data plays while making critical decisions, we can be certain that data science is a lucrative and excellent career choice. It offers tremendous opportunities in different industries with handsome salary packages.

Ensuring that our courses are budget-friendly, we have kept the fee of our data science course competitive. You have to pay only INR 29,500 to enrol in our classes, and the fee includes live training, videos, PDFs, and other study materials.

Become a successful Data Scientist by learning from industry experts!

Start learning now with WsCube Tech.

Book your free class of the best Data Science Course in Jaipur!

  • 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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