DATA SCIENCE USING PYTHON TRAINING IN Jaipur | DATA SCIENCE WITH PYTHON

Rated 5/5 (2700+ Reviews)
★ ★ ★ ★ ★

Are you Looking for the Best Institute for Data Science using Python training in Jaipur?

Groot Academy offers Data Science using Python training classes with live projects by expert trainers in Jaipur.

Our Data Science using Python training program in Jaipur is specially designed for Undergraduates (UG), Graduates, working professionals, and also for Freelancers. We provide end-to-end learning on Data Science using Python Domain with deeper dives for creating a winning career for every profile. If you are looking for a high-quality Science training program that will give you the skills you need to succeed in the real world, thenGroot-Academy The IT Training School is the perfect place. ContactGroot-Academy at 8690047091 for more about their excellent job-oriented Science training program.

Why learn Data Science using Python?

It's continued to be a great option for data scientists who use it for building Machine learning applications or using them and other scientific computations.

Data Science Using Python Training in Jaipur cuts development time in half with its simple-to-read syntax and easy compilation feature with the easy-to-learn concept.

Debugging any type of program is a breeze in this language with its built-in debugger.

It runs on every famous type of platform like Windows, Linux/Unix, and Mac OS and has been ported to Java and .NET virtual machines.

Python is free to use language, even for commercial products, because of its OSI-approved open-source license, so anyone can use it for free.

It has been opted as the most preferred Language for Data Science and the increasing search trends on Python every day also indicate that it is the "Next Big Thing" and a must for aspirants in the Data Science field.

Important:Data Science Training Institute

Introduction

The capacity to extract actionable insights from enormous amounts of data has become essential for businesses across industries in today's data-driven environment.

This growing demand for skilled data sciences and professionals has led numerous Science training institutes to emerge.

One such institute that stands out isGroot-Academy, The IT Training School, known for its comprehensive and industry-focused Science programs.

The vast range of courses offered by Science Training Institute is created to give students the knowledge and skills they need to succeed in data Science.

The institute addresses various issues, from fundamental ideas to cutting-edge methods, including data visualization, statistical analysis, machine learning, predictive modelling, and more.

The curriculum is carefully crafted to meet the evolving industry demands and ensure learners are well-prepared to tackle real-world Science challenges.

There are many different Science training institutes available, but not all of them are created equal.

Groot, The IT Training School, is one of India's leading Science training institutes, having a physical presence in Delhi, Jaipur, Gurgaon and Ghaziabad.

The institute's extensive curriculum covers all of the fundamental competencies needed for a career in Science.

Data Science Training Importance

Groot's Science training program is designed to help you develop the skills you need to succeed in the real world.

The program includes hands-on training with real-world data sets so that you can apply your skills to real-world problems.

In addition to the technical skills,Groot-Academy also teaches you the soft skills essential for success in Science. These skills include communication, teamwork, and problem-solving.

Groot's Science training program is also highly practical. The program includes a capstone project, where you can apply your skills to a real-world problem.

You will have the opportunity to show potential employers your talents through this project.

Additionally,Groot-Academy helps its learners find jobs.

The institute has a strong network of corporate partners who are always looking for talented data sciences and scientists.

With the help ofGroot-Academy, you can be confident that you can find a job after completing the program.

If you are serious about a career in Science, I encourage you to be admitted toGroot-Academy IT Training School.

The institute's comprehensive curriculum, hands-on training, and placement assistance will give you the skills and knowledge you need to succeed in this exciting field.

Benefits of training fromGroot-Academy

Here are some of the benefits of taking an Science course atGroot-Academy The IT Training School:

You will learn from Industry-experienced and qualified instructors who are experts in their respective fields.

You will have use of the updated tools and numerous applications.

Networking opportunities with academic and professional peers are available.

You will receive an industry-recognised course completion certificate.

Why To Enroll In Our Data Science Using Python Training Course in Jaipur?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, and Opening the doors of opportunities.

Our Data Science using Python Trainees are working across the nation. We atGroot-Academy India, The #1 Data Science using Python Course in Jaipur with 100% Placement.

Certified Trainers with Over 10,000 Students Trained in Data Science using Python Course in Jaipur.

Why Groot-Academy?

  • Groot has a dedicated team of highly expert trainers to identify, evaluate, implement, and provide the Best Data Science Using Python Training Institute in Jaipur for our students.
  • Our Trainers leverage a defined methodology that helps identify opportunities, develop the most optimal resolution and maturely execute the solution.
  • We have the best trainers across the world to provide the Best Data Science Using Python Training in Jaipur who are highly qualified and are the best in their field.
  • The Training & Placement cell is committed to providing all attainable help to the students in their efforts to seek out employment and internships in every field.
  • The placement department works beside alternative departments as a team in moulding the scholars to the necessities of varied industries.
  • We have proactive and business-clued-in Placement Cells that pride themselves on a robust skilled network across numerous sectors.
  • It actively coordinates with every student and ensures that they get placed with purported MNCs within six months of graduating. We are the Best Data Science Using Python Training Institute in Jaipur.

What Our Students Will Get During Data Science Using Python Training Course?

Get dedicated student support, career services, industry expert mentors, and real-world projects. Career Counselling. Timely Doubt Resolution. 50% Salary Hike, Career Counselling Case Studies + Tools + Certificate.

Why To Enrol In Our Mern Stack Training Course in Jaipur?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, and Opening the doors of opportunities. Our Mern Stack Trainees are working across the nation. We at Groot Academy India, No#1 Mern Course in Jaipur with 100% Placement. Certified Trainers with Over 10,000 Students Trained in Online Mern Stack Course in Jaipur.

ENROOL NEW
Key Takeaways: Mern stack developers can efficiently build web and mobile applications using standard processes and abilities. It implements design thinking with enhanced expertise in various technologies. In the coming years, MERN Stack is highly likely to be used extensively since more and more applications are now built-in in JavaScript.

Reasons To Choose Groot Academy For Mern Stack Developer

Frequently Asked Data Science Interview Questions

1:What is data science?

Ans: Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.

2:What are the key skills required for a career in data science?

Ans: Key skills include programming (e.g., Python, R), statistical analysis, machine learning, data cleaning and preprocessing, data visualization, and domain-specific knowledge.

3:How does data science differ from traditional statistics?

Ans: While both involve analyzing data, data science often deals with larger datasets, incorporates machine learning for predictive modeling, and focuses on extracting actionable insights from data.

4: What is the CRISP-DM methodology in data science?

Ans: CRISP-DM (Cross-Industry Standard Process for Data Mining) is a widely used methodology in data science that outlines the steps involved in a data mining or analytics project, including business understanding, data preparation, modeling, evaluation, and deployment.

5:Explain the concept of machine learning in data science.

Ans:Machine learning is a subset of artificial intelligence that involves the development of algorithms and models that enable systems to learn and make predictions or decisions without being explicitly programmed.

6: What is the significance of feature engineering in machine learning?

Ans: Feature engineering involves selecting and transforming variables to improve model performance. It is a critical step in building effective machine learning models.

7:How is data science used in business?

Ans:Data science helps businesses make data-driven decisions, optimize processes, improve customer experiences, and gain a competitive edge through insights derived from data analysis.

8:What is the role of a data scientist in an organization?

Ans: Data scientists are responsible for collecting, analyzing, and interpreting complex datasets. They develop models, algorithms, and statistical analyses to extract valuable insights and inform decision-making.

9:How does data science contribute to artificial intelligence (AI)?

Ans: Data science provides the foundation for AI by utilizing data to train and improve machine learning models, which are a key component of many AI applications.

10:What are the ethical considerations in data science?

Ans: Ethical considerations in data science include issues related to privacy, bias in algorithms, transparency, and the responsible use of data, ensuring that the impact of data science activities is fair and equitable.

Course content

Introduction

  • Introduction to Data Analytics
  • Introduction to Business Analytics
  • Understanding Business Applications
  • Data types and data Models
  • Type of Business Analytics
  • Evolution of Analytics
  • Data Science Components
  • Data Scientist Skillset
  • Univariate Data Analysis
  • Introduction to Sampling

Basic Operations in R Programming

  • Introduction to R programming
  • Types of Objects in R
  • Naming standards in R
  • Creating Objects in R
  • Data Structure in R
  • Matrix, Data Frame, String, Vectors
  • Understanding Vectors & Data input in R
  • Lists, Data Elements
  • Creating Data Files using R

Data Handling in R Programming

  • Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
  • Sub-setting Data
  • Selecting (Keeping) Variables
  • Excluding (Dropping) Variables
  • Selecting Observations and Selection using Subset Function
  • Merging Data
  • Sorting Data
  • Adding Rows
  • Visualization using R
  • Data Type Conversion
  • Built-In Numeric Functions
  • Built-In Character Functions
  • User Built Functions
  • Control Structures
  • Loop Functions

Introduction to Statistics

  • Basic Statistics
  • Measure of central tendency
  • Types of Distributions
  • Anova
  • F-Test
  • Central Limit Theorem & applications
  • Types of variables
  • Relationships between variables
  • Central Tendency
  • Measures of Central Tendency
  • Kurtosis
  • Skewness
  • Arithmetic Mean / Average
  • Merits & Demerits of Arithmetic Mean
  • Mode, Merits & Demerits of Mode
  • Median, Merits & Demerits of Median
  • Range
  • Concept of Quantiles, Quartiles, percentile
  • Standard Deviation
  • Variance
  • Calculate Variance
  • Covariance
  • Correlation

Introduction to Statistics – 2

  • Hypothesis Testing
  • Multiple Linear Regression
  • Logistic Regression
  • Market Basket Analysis
  • Clustering (Hierarchical Clustering & K-means Clustering)
  • Classification (Decision Trees)
  • Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)

Introduction to Probability

  • Standard Normal Distribution
  • Normal Distribution
  • Geometric Distribution
  • Poisson Distribution
  • Binomial Distribution
  • Parameters vs. Statistics
  • Probability Mass Function
  • Random Variable
  • Conditional Probability and Independence
  • Unions and Intersections
  • Finding Probability of dataset
  • Probability Terminology
  • Probability Distributions

Data Visualization Techniques

  • Bubble Chart
  • Sparklines
  • Waterfall chart
  • Box Plot
  • Line Charts
  • Frequency Chart
  • Bimodal & Multimodal Histograms
  • Histograms
  • Scatter Plot
  • Pie Chart
  • Bar Graph
  • Line Graph

Introduction to Machine Learning

  • Overview & Terminologies
  • What is Machine Learning?
  • Why Learn?
  • When is Learning required?
  • Data Mining
  • Application Areas and Roles
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement learning

Machine Learning Concepts & Terminologies

  • Key tasks of Machine Learning
  • Modelling Terminologies
  • Learning a Class from Examples
  • Probability and Inference
  • PAC (Probably Approximately Correct) Learning
  • Noise
  • Noise and Model Complexity
  • Triple Trade-Off
  • Association Rules
  • Association Measures

Regression Techniques

  • Concept of Regression
  • Best Fitting line
  • Simple Linear Regression
  • Building regression models using excel
  • Coefficient of determination (R- Squared)
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Variable transformation
  • Reading coefficients in MLR
  • Multicollinearity
  • VIF
  • Methods of building Linear regression model in R
  • Model validation techniques
  • Cooks Distance
  • Q-Q Plot
  • Durbin- Watson Test
  • Kolmogorov-Smirnof Test
  • Homoskedasticity of error terms
  • Logistic Regression
  • Applications of logistic regression
  • Concept of odds
  • Concept of Odds Ratio
  • Derivation of logistic regression equation
  • Interpretation of logistic regression output
  • Model building for logistic regression
  • Model validations
  • Confusion Matrix
  • Concept of ROC/AOC Curve
  • KS Test

Market Basket Analysis

  • Applications of Market Basket Analysis
  • What is association Rules
  • Overview of Apriori algorithm
  • Key terminologies in MBA
  • Support
  • Confidence
  • Lift
  • Model building for MBA
  • Transforming sales data to suit MBA
  • MBA Rule selection
  • Ensemble modelling applications using MBA

Time Series Analysis (Forecasting)

  • Model building using ARIMA, ARIMAX, SARIMAX
  • Data De-trending & data differencing
  • KPSS Test
  • Dickey Fuller Test
  • Concept of stationarity
  • Model building using exponential smoothing
  • Model building using simple moving average
  • Time series analysis techniques
  • Components of time series
  • Prerequisites for time series analysis
  • Concept of Time series data
  • Applications of Forecasting

Decision Trees using R

  • Understanding the Concept
  • Internal decision nodes
  • Terminal leaves.
  • Tree induction: Construction of the tree
  • Classification Trees
  • Entropy
  • Selecting Attribute
  • Information Gain
  • Partially learned tree
  • Overfitting
  • Causes for over fitting
  • Overfitting Prevention (Pruning) Methods
  • Reduced Error Pruning
  • Decision trees – Advantages & Drawbacks
  • Ensemble Models

K Means Clustering

  • Parametric Methods Recap
  • Clustering
  • Direct Clustering Method
  • Mixture densities
  • Classes v/s Clusters
  • Hierarchical Clustering
  • Dendogram interpretation
  • Non-Hierarchical Clustering
  • K-Means
  • Distance Metrics
  • K-Means Algorithm
  • K-Means Objective
  • Color Quantization
  • Vector Quantization

Tableau Analytics

  • Tableau Introduction
  • Data connection to Tableau
  • Calculated fields, hierarchy, parameters, sets, groups in Tableau
  • Various visualizations Techniques in Tableau
  • Map based visualization using Tableau
  • Reference Lines
  • Adding Totals, sub totals, Captions
  • Advanced Formatting Options
  • Using Combined Field
  • Show Filter & Use various filter options
  • Data Sorting
  • Create Combined Field
  • Table Calculations
  • Creating Tableau Dashboard
  • Action Filters
  • Creating Story using Tableau

Analytics using Tableau

  • Clustering using Tableau
  • Time series analysis using Tableau
  • Simple Linear Regression using Tableau

Frequently Asked Questions (FAQ)

1: What is data Science?

Ans:Data Science is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.

2:Why is data Science important?

Ans: Data Science helps organizations gain insights, make informed decisions, identify patterns and trends, optimize processes, and ultimately improve business performance.

3: What are the key components of data Science?

Ans:The key components include data collection, data processing, data storage, analysis, and interpretation. Techniques like statistical analysis, machine learning, and data visualization are often employed.

4: What is the difference between descriptive, predictive, and prescriptive Science?

Ans: Descriptive Science focuses on summarizing historical data, predictive Science involves forecasting future trends, and prescriptive Science recommends actions based on the analysis of available data.

5: How is big data related to data Science?

Ans:Big data refers to the large volume, velocity, and variety of data, while data Science deals with extracting meaningful insights from data. Big data Science involves specialized techniques to handle and analyze massive datasets.

6:What role does machine learning play in data Science?

Ans: Machine learning is a subset of data Science that involves the use of algorithms and statistical models to enable systems to improve their performance on a task over time without being explicitly programmed.

7: How is data Science used in business?

Ans: Businesses use data Science to understand customer behavior, optimize marketing strategies, improve operational efficiency, identify opportunities for growth, and mitigate risks.

8:What are some common challenges in data Science?

Ans: Challenges include data quality issues, privacy concerns, the need for skilled professionals, choosing appropriate analytical techniques, and staying current with rapidly evolving technologies.

9:How is data visualization important in data Science?

Ans:Data visualization is crucial for communicating insights effectively. It helps in making complex data more understandable and enables decision-makers to grasp patterns and trends quickly.

10:What ethical considerations are associated with data Science?

Ans: Ethical considerations include issues related to privacy, consent, bias in algorithms, and the responsible use of data. Ensuring fairness and transparency in Science processes is essential.

Why should you learn Data Science?


Learning data Science is highly valuable in today's data-driven world for several compelling reasons. First and foremost, data sciences play a crucial role in extracting meaningful insights from vast and complex datasets, empowering organizations to make informed decisions. As businesses increasingly rely on data to drive strategies, understanding data Science allows individuals to contribute to optimizing processes, identifying trends, and solving real-world problems. Moreover, data sciences are in high demand across various industries, offering excellent career prospects. By acquiring data Science skills, one gains proficiency in tools and techniques for data manipulation, statistical analysis, and visualization, enhancing both problem-solving abilities and critical thinking. Overall, learning data Science not only opens doors to diverse career opportunities but also equips individuals with the skills needed to navigate and succeed in the data-centric landscape of the modern workplace.
Jobs for Data Scientist


Data science has become integral to various industries, creating a demand for professionals with expertise in extracting valuable insights from large and complex datasets. Job opportunities in data science span a range of roles, including data sciences, data scientists, machine learning engineers, and data engineers. Data sciences focus on interpreting and visualizing data to aid decision-making, while data scientists employ statistical techniques and machine learning algorithms to derive actionable insights. Machine learning engineers develop and deploy predictive models, and data engineers design and manage the infrastructure for data generation and storage. Industries such as finance, healthcare, e-commerce, and technology are actively seeking data science talent to enhance their operations, optimize processes, and gain a competitive edge through informed decision-making. As the field continues to evolve.
Training and Placement


Data science training and placement programs provide individuals with the essential skills and knowledge needed to excel in the field of data Science. These programs typically cover a range of topics, including data manipulation, statistical analysis, programming languages like Python and R, and data visualization techniques. Hands-on, practical experience with real-world datasets is often a core component of the training. Many training programs also focus on industry-relevant tools and technologies such as SQL, Excel, and data visualization tools like Tableau or Power BI. Beyond technical skills, these programs may emphasize critical thinking, problem-solving, and effective communication of analytical findings. Successful completion of a data science training program enhances one's employability and opens doors to various industries, including finance, healthcare, marketing, and technology. Many training programs also offer placement assistance, connecting graduates with job opportunities and providing a pathway to launch a successful career as a data science.