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Skill India


Get a head start on your career with Edusoft's's top-rated Data Science course in Lakshmi Nagar , Delhi. The course comes with a guaranteed job referral, so you can be confident in your future job prospects. Plus, the course is created by industry experts and focuses on providing practical, hands-on learning experiences. You'll gain valuable skills and knowledge that will set you apart from the competition.

1. Python for Data Science

Python Basics

  • Need for Programming
  • Advantages of Programming
  • Overview of Python
  • Organizations using Python
  • Python Applications in Various Domains
  • Python Installation
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments

Data Structure and File Operations

  • Method of Accepting User Input and eval Function
  • Python - Files Input/Output Functions
  • Lists and Related Operations
  • Tuples and Related Operations
  • Strings and Related Operations
  • Sets and Related Operations
  • Dictionaries and Related Operations

Functions and Object Oriented Programming

  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name =” main ”
  • Function Parameters
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Variable Scope and Returning Values
  • Lambda Functions
  • Various Built-In Functions
  • Introduction to Object-Oriented Concepts
  • Built-In Class Attributes
  • Public, Protected and Private Attributes, and Methods
  • Class Variable and Instance Variable
  • Constructor and Destructor
  • Decorator in Python
  • Core Object-Oriented Principles
  • Inheritance and Its Types
  • Method Resolution Order
  • Overloading
  • Overriding
  • Getter and Setter Methods
  • Inheritance-In-Class Case Study

Working with Modules and Handling Exceptions

  • Standard Libraries
  • Packages and Import Statements
  • Topics : Working with Modules and Handling Exceptions
  • Info@Edusoft' | +91-7701928515 | www.Edusoft'
  • Reload Function
  • Important Modules in Python
  • Sys Module
  • Os Module
  • Math Module
  • Date-Time Module
  • Random Module
  • JSON Module
  • Regular Expression
  • Exception Handling

Introduction to NumPy

  • Basics of Data Analysis
  • NumPy - Arrays
  • Operations on Arrays
  • Indexing Slicing and Iterating
  • NumPy ArrayAttributes
  • Matrix Product
  • NumPy Functions
  • Functions
  • Array Manipulation
  • File Handling Using NumPy
  • Array Creation and Logic Functions
  • File Handling Using Numpy

Data Manipulation using pandas

  • Introduction to pandas
  • Data structures in pandas
  • Series
  • Data Frames
  • Importing and Exporting Files in Python
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • Types of Joins on Data Objects
  • Data Cleaning using pandas
  • Exploring Datasets

2. Data Science Primer and Statistics

Basics of Data Science

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Application of Data Science

Exploratory Data Analysis

  • Introduction
  • Stages of Analytics
  • CRISP DM Data Life Cycle
  • Data Types
  • Introduction to EDA
  • First Business Moment Decision
  • Second Business Moment Decision
  • Third Business Moment Decision
  • Fourth Business Moment Decision
  • Correlation

Feature Engineering

  • What is Feature
  • Feature Engineering
  • Feature Engineering Process
  • Benefit
  • Feature Engineering Techniques

Inferential Statistics & Hypothesis Testing

  • Basics Of Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Central Limit Theorem
  • Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making
  • A Decision, And Critical Value Method
  • Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors
  • Industry Demonstration Of Hypothesis Testing: Two-Sample Mean And
  • Proportion Test, A/B Testing

3. Machine Learning

Linear Regression

  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression

Linear Regression

  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression

Logistic Regression

  • Univariate Logistic Regression
  • Multivariate Logistic Regression: Model
  • Building And Evaluation
  • Logistic Regression:
  • Industry Applications

KNN classifier

  • Data mining classifier technique
  • Application of KNN classifier
  • Lazy learner classifier
  • Altering hyperparameter(k) for better accuracy

Support Vector classifier

  • Black box
  • SVM hyperplane
  • Max margin hyperplane
  • Kernel tricks for non linear spaces

Decision Tree Classifier

  • Rule based classification method
  • Different nodes for develop decision trees
  • Discretization
  • Entropy
  • Greedy approach
  • Information gain

Ensemble Learning

  • Challenges with standalone model
  • Reliability and performance of a standalone model
  • Homogeneous & Heterogeneous Ensemble Technique
  • Bagging & Boosting
  • Random forest
  • Stacking
  • Voting & Averaging technique

Time Series Analysis

  • Difference between cross sectional and time series data
  • Different component of time series data
  • Visualization techniques for time series data
  • Model based approach
  • Data driven based approach


  • Difference between Supervised and Unsupervised Learning
  • Prelims of clustering
  • Measuring distance between record and groups
  • Linkage functions
  • Dendrogram

Dimensionality Reduction

  • Dimension reduction
  • Application of PCA
  • PCA & its working
  • SVD & its working

Market Basket Analysis

  • Point of Sale
  • Application of Association rules
  • Measure of association rules
  • Drawback of measure of association rules
  • Condition probability
  • Lift ratio

4. Deep Learning

Introduction to Perceptron, Multilayer Perceptron/ANN

  • Black box techniques
  • Intution of neural networks
  • Perceptron algorithm
  • Calculation of new weights
  • Non linear boundaries in MLP
  • Integration function
  • Activation function
  • Error surface
  • Gradient descent algo

Deep Learning Black Box Technique – CNN, RNN

  • Imagenet classification challenges
  • Convolution network applications
  • Challenges in classifying the images using MLP
  • Parameter explosion
  • Pooling layers
  • Fully connected layers
  • Alexnet case study
  • Modelling sequence data
  • Vanishing/Gradient descent explode

Platforms for Deep Learning & Deep Learning Software Libraries

  • What is a Deep Learning Platform?
  • Dato GraphLab
  • What is a Deep Learning Library?
  • Theano
  • Deeplearning4j
  • Torch
  • Caffe

5. Data Visualization and Story Telling

Basic Visualization Tools

  • Bar Charts
  • Histograms
  • Pie Charts
  • Box Plots

Basic Visualization Tools Continued

  • Scatter Plots
  • Line Plots and Regression

Specialized Visualization Tools

  • Pair plot
  • Word Clouds
  • Radar Charts
  • Waffle Charts
  • 6. Natural Language Processing

    Text Mining & Natural Language Processing

    • Text data generating sources
    • How to give structure to text structure using bag of words
    • Terminology used in text data analysis
    • DTM & TDM
    • TFIDF & its usage
    • Word cloud and its interpretation

    7. SQL

    Getting Started and Creating, Selecting & Retrieving Data with SQL

    • Introduction to Databases
    • How to create a Database instance on Cloud?
    • Provision a Cloud hosted Database instance.
    • What is SQL?
    • Thinking About Your Data
    • Relational vs. Transactional Models ER Diagram
    • CREATE Table Statement and DROP tables
    • UPDATE and DELETE Statements
    • Retrieving Data with a SELECT Statement
    • Creating Temporary Tables
    • Adding Comments to SQL

    Filtering, Sorting, and Calculating Data with SQL

    • Basics of Filtering with SQL
    • Advanced Filtering: IN, OR, and NOT
    • Using Wildcards in SQL
    • Sorting with ORDER BY
    • Math Operations
    • Aggregate Functions
    • Grouping Data with SQL

    Subqueries and Joins in SQL

    • Using Subqueries
    • Subquery Best Practices and Considerations
    • Joining Tables
    • Cartesian (Cross) Joins
    • Inner Joins
    • Aliases and Self Joins
    • Advanced Joins: Left, Right, and Full Outer Joins
    • Unions

    Modifying and Analyzing Data with SQL

    • Working with Text Strings
    • Working with Date and Time Strings
    • Date and Time Strings Examples
    • Case Statements
    • Views
    • Data Governance and Profiling
    • Using SQL for Data Science

    Accessing Databases using Python

    • How to access databases using Python?
    • Writing code using DB-API
    • Connecting to a database using DB API
    • Create Database Credentials
    • Connecting to a database instance
    • Creating tables, loading, inserting, data and querying data
    • Analysing data with Python

    8. Excel

    Analyzing and Visualizing Data using Excel

    • Input data & handling large spreadsheets
    • Tricks to get your work done faster
    • Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)
    • Transforming messy data into shape
    • Cleaning, Processing and Organizing large data
    • Spreadsheet design principles
    • Drop-down lists in Excel and adding data validation to the cells.
    • Creating Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers and Timelines
    • Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.
    • Excel features: - Sort, Filter, Search & Replace Go to Special etc...
    • Importing and Transforming data (with Power Query)
    • Customize the Microsoft Excel interface
    • Formatting correctly for professional reports.
    • Commenting on cells.
    • Automate data entry with Autofill and Flash-fill.
    • Writing Excel formulas & referencing to other workbooks / worksheets.
    • Printing options
    • Charts beyond column and bar charts: - Pareto chart, Histogram, Treemap, Sunburst
    • charts & more

    9. TABLEAU

    Analyzing and Visualizing Data using Tableau

    • Introduction to Data Visualization
    • Tableau Introduction and Tableau Architecture
    • Exploring Data using Tableau
    • Working with Data using Tableau including Data Extraction and
    • Blending
    • Various Charts in Tableau(Basics to Advanced)
    • Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
    • Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau

    Program Highlights

    350+ Live sessions

    15+ Industry Projects

    Life time accessibility

    Live project experience

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    All Trainer

    About Us

    Data science is a field that uses statistical analysis, programming, and domain expertise to find useful information in data. Data scientists are highly skilled professionals who use these techniques to provide actionable insights that can be used to make better decisions and improve business outcomes. They usually have strong educational backgrounds in mathematics, computer science, and related fields, as well as practical experience in areas like statistics and machine learning. They use this knowledge to process, analyze, and interpret large and complex data sets, and then apply their insights to help organizations make better decisions and achieve their goals. ...

    What is a data science course and why should you take one?

    The demand for data scientists is rapidly increasing across industries, from healthcare to business, marketing, technology, and finance. Learning data science skills can open up exciting career opportunities and boost your earning potential. It's a great time to join this fast-growing field and shape the future of data-driven decision-making.
    Our data science course will help you gain the skills and knowledge that employers look for in this rapidly growing field. You'll become a strong candidate with both theoretical knowledge and practical experience, making you a great choice for employers.

    Why should you choose Edusoft's's data science course?

    In a competitive market for data science education, Edusoft's stands out with our unique approach to training. We offer a personalized curriculum and practical, hands-on experience to ensure that our students gain the skills and knowledge they need to succeed in the field. Our approach includes live sessions and simulated interviews to prepare our students for real-world challenges.
    With our Microsoft-certified data science program, you'll gain valuable industry-recognized credentials that will give you a competitive edge. Plus, with our real-world project experience and flexible learning options, you can learn at your own pace. Our program is designed to meet the needs of busy professionals and students alike.

    Assured advantages of our out-standing data science course

    Our data science program covers all aspects of the field, from introductory to advanced topics. You'll learn how to use popular programming languages and tools, including Python, C/C++, machine learning, deep learning, and applied statistics. This comprehensive curriculum will prepare you for a wide range of real-world data science projects.
    Our program strikes the perfect balance between theoretical knowledge and practical experience. You'll get hands-on experience with real-world data science projects, such as predicting credit default and analyzing YouTube video analytics. In addition to our 350+ live sessions, you'll also have the opportunity to participate in simulated interviews, preparing you for success in the job market.

    What is the ideal course curriculum for data science course?

    The data science course curriculum includes topics such as data preprocessing, data mining, machine learning algorithms, deep learning, natural language processing, and more. You'll also learn how to work with various programming languages, such as Python, SQL, and R. And you'll gain practical experience with industry-relevant projects.

    Who is eligible for this data science course?

    This course is designed for anyone who wants to gain skills in data science, including recent graduates, experienced professionals, and career changers. You don't need any prior experience in data science or programming to enroll in the course. However, a basic understanding of mathematics, statistics, and computers is recommended.

    Why is it beneficial to take a data science course? What are the potential career benefits of earning a data science certification?

    Data science is a highly in-demand skill, and taking a data science course can be an excellent way to gain the knowledge and skills necessary to pursue a career in this exciting field. A data science course typically covers essential topics like programming languages, statistics, and machine learning. It also provides students with opportunities to gain hands-on experience working with real-world data. This experience can help students understand how data science can be used to solve business problems and make better decisions.
    Online data science certification programs can vary in terms of their value and relevance to an individual's career goals. In general, these programs can be useful for demonstrating competence in data science, which may be beneficial for job seekers or those looking to advance in their current roles. However, it's important to research specific programs to ensure they align with one's goals. While a data science certification can be a valuable addition to one's resume, it's important to recognize that it is not a magic bullet for career advancement. Employers will also consider an individual's experience and portfolio of work. Therefore, individuals should consider the time and effort required to earn a certification and whether it aligns with their long-term career goals.

    What incentives one can relish after finishing the course?

    Upon completing a course at Edusoft's, students will receive not only a Microsoft certification, but also guaranteed job referrals. These referrals may include opportunities in various industries such as business, finance, healthcare, and technology. Students can use these referrals to pursue a career path
    that aligns with their interests and skills. With the right training and dedication, they can become successful-

    • Data science professionals
    • Data analyst
    • Data engineer
    • Statistician
    • Data architect
    • Business analyst and more

    Perks of getting a data science course

    There are many benefits to our data science certification course, kindly go through the below mentioned bullets to get the finest idea.

    • A comprehensive and high-quality course curriculum.
    • You'll have the opportunity to communicate with industry experts to create a personalized curriculum tailored to your needs and interests.
    • Gain valuable real-world experience with 15+ industry projects across 6+ different domains.
    • Our live interactive sessions and recorded videos give you the flexibility to learn at your own pace.
    • This course is designed to be completed in just five months, but you'll have lifetime access to the materials and resources.
    • Our simulated interview sessions help you prepare for the real world and give you the confidence to succeed in a job interview.
    • You'll earn two certifications - one from Microsoft and one from a leading company. These certifications will validate your skills and give you a competitive edge in the job market.
    • You'll receive guaranteed job referrals to help you land your dream job.
    • You can choose between an online or an offline course, depending on your preferences and availability.
    • This course is designed to be completed in just five months, but you'll have lifetime access to the materials and resources.

    What core skills and projects will be focused upon in this data science course?

    Our course is designed to give students real-world experience through hands-on projects in a variety of industries. With 15+ projects spanning 6+ domains, students will gain practical knowledge and skills that can be applied in the workplace. Our course covers programming fundamentals, data science projects in Python, and various statistical and analytical concepts. It also includes training in over 20 popular data science tools. This comprehensive curriculum is designed to equip students with the skills and knowledge they need to succeed in the field.

    How long this data science course will stretch?

    The duration of data science courses can vary widely, depending on the institute and the format of the course. Typically, online courses range from 5 months to 3 years, while offline courses may be shorter or longer. The duration also depends on the individual's learning goals and needs.

    How much a data science course cost?

    Our comprehensive data science course comes at the extremely reasonable price of 65,000 rupees with feasible payment options. We encourage you to explore all the available options to find the best fit for your needs.

    Prediction of the future of data science industry

    The data science industry is poised for significant growth in the coming years, with many experts predicting strong demand for skilled professionals. The industry is expected to experience rapid expansion due to the increasing use of big data and the need for data-driven decision-making. Experts predict significant growth in the data science industry in the coming years, driven by the increasing need for data-driven decision-making and the growing demand for skilled professionals.
    The data science industry is expected to grow rapidly in the coming years due to a few key factors. First, the increasing amount of data being generated by businesses and individuals is creating a need for data-driven decision-making. Second, advances in computing power and storage capacity are making it easier to process and analyze large amounts of data. Third, the emergence of new technologies like machine learning and artificial intelligence is making it possible to extract insights from data in new and innovative ways.
    One example is in the field of healthcare, where data science is being used to analyze patient data and improve diagnosis and treatment. In finance, data science is being used to detect fraud and optimize investment strategies. In retail, it's being used for personalized marketing and product recommendations.All of these factors are driving demand for skilled data scientists and fueling the growth of the data science industry.

    How distinctive is to get the online data science course than an offline setting?

    There are a few key differences between online and offline data science courses. For one, online courses tend to be more flexible in terms of scheduling, since you can access the course materials and lectures at any time, from any location. There is research that supports both sides of the argument. Studies have shown that online learning can be just as effective as in-person learning in terms of knowledge retention and test scores. However, other studies have shown that in-person learning can lead to better long-term retention and deeper understanding of the material. One possible reason for this is that in-person learning allows for more opportunities to interact with the instructor and ask questions.

    How huge the hunger is there in India for data science course?

    In India, the field of data science is gaining significant attention and experiencing a growing demand due to the increased need for data analysis in many industries, particularly in the business, finance, and healthcare sectors. This demand is driven by the need to effectively collect and manage large amounts of data, especially in the aftermath of the COVID-19 pandemic
    In India, data is recognized as a valuable resource, and the demand for skilled data scientists is on the rise. As a result, people are increasingly interested in pursuing online data science courses to gain the necessary skills and capitalize on the abundant opportunities in the field. With the convenience and flexibility of online learning, people are able to upskill themselves without disrupting their current work or lifestyle.

    How much as a data scientist one can earn nationally and internationally?

    It really depends on several factors, including the country you're working in, your level of experience, and the specific industry you're working in. In general, data scientists can earn a very good salary both in India and internationally. In India, the average salary for a data scientist can range from ₹5 lakhs to ₹15 lakhs per year, with the potential to earn even more with experience and expertise.
    Internationally, as I mentioned, the average salary for a data scientist can range from $60,000 to $150,000, with experienced professionals earning even more. There is also a lot of variation depending on the specific country, industry, and company. In general, the tech hubs of the world like the US, UK, and Canada tend to have higher salaries for data scientists.

    What is the difference between AI and data science course?

    The key difference between AI and data science courses is that AI courses focus specifically on the design and development of artificial intelligence systems, while data science courses cover a broader range of topics related to working with and analyzing data. Data science courses also tend to cover more general programming and mathematical concepts, while AI courses focus more on machine learning and neural networks.

    Benefits of getting the AI and data science course

    The benefits of taking AI and data science courses include gaining in-demand skills, increasing your career opportunities, and being able to work with cutting-edge technology. These courses can also help you develop critical thinking and problem-solving skills, and give you the ability to make data-driven decisions. Additionally, they can be a lot of fun and can open up new and exciting career paths.

    Benefits of getting the AI and data science course

    The benefits of taking AI and data science courses include gaining in-demand skills, increasing your career opportunities, and being able to work with cutting-edge technology. These courses can also help you develop critical thinking and problem-solving skills, and give you the ability to make data-driven decisions. Additionally, they can be a lot of fun and can open up new and exciting career paths.

    How huge is the scope for AI and data science course?

    The scope for AI and data science courses is huge! The demand for professionals with these skills is growing rapidly, and it's expected to continue growing in the coming years. There are many different industries and sectors that are looking for professionals with AI and data science skills, including healthcare, finance, retail, and manufacturing. So, the career options are diverse and plentiful.
    And the skills learned in these courses can be applied in a wide range of settings. For example, in the healthcare industry, AI and data science can be used to analyze patient data and improve diagnoses and treatments. In finance, they can be used for risk management and fraud detection. In retail, they can be used for personalized recommendations and targeted marketing. And the list goes on and on.
    In addition to the wide range of industries that use AI and data science, there are also many different job roles that require these skills. For example, there are data scientists, who analyze and interpret data, data engineers, who build the infrastructure for data analysis, and machine learning engineers, who develop and train machine learning models.
    There are also many other roles related to AI and data science, such as business analysts, who use data to make informed decisions, and software developers, who create applications that use data and AI. The demand for these roles is increasing, and salaries for these positions are often very competitive. The future of AI and data science is bright and full of opportunity!

    How to get admission in the AI and data science course?

    If you're interested in enrolling in an AI and data science course, you can typically do so by visiting the course provider's website, browsing their catalog of courses, and selecting the one that best matches your interests and skill level. Once you've selected a course, you can typically follow the provider's enrollment process and pay for the course to gain access to the materials and begin your learning journey.

    Frequently Asked Questions

    Learn from experts in live-interactive classes under Advanced data science and AI course with Guaranteed job referral.

    Frequently Asked Questions

    Absolutely! While having a coding background can certainly help, it's not a requirement for most data science programs. Many programs are designed to be accessible to people from a wide range of backgrounds, including those without previous coding experience. The key thing is a strong foundation in mathematics, statistics, and an interest in working with data. Many programs offer introductory courses in programming languages like Python, so you'll be able to pick up the coding skills you need as you go along. So don't let a lack of coding background hold you back - the field of data science is open to you!

    Typically, there is no strict eligibility limit for data science programs. However, some programs may have certain prerequisites or expectations of prior experience or education, such as a bachelor's degree in a related field like computer science, mathematics, or statistics. But, many programs are open to those with a variety of backgrounds (just as Edusoft's), as long as they are motivated and willing to learn. The only real "limit" might be an ability and willingness to work with data, statistics, and programming.

    Data science projects are a big part of the learning process and can cover a wide range of topics. Here are a few examples of projects you might see in a data science program:

    • Predicting customer churn for a company based on data about past customer behavior
    • Analyzing website traffic data to optimize user experience
    • Using machine learning to detect fraudulent credit card transactions
    • Creating a recommendation engine for e-commerce sites
    • Analyzing financial data to predict stock market trends
    • Identifying patterns in social media data to understand consumer preferences
    • Forecasting demand for products based on sales data and trends.

    Absolutely we understand that life happens, and that’s exactly why we have a policy in place for students who have to miss a class due to an emergency. Here are a few options you might see:

    • Recorded lectures or class materials that you can access at a later time
    • Office hours with the instructor or TA for one-on-one help
    • Make-up assignments or quizzes to catch up on missed material
    • The opportunity to retake the class or a portion of it in a future semester

    Edusoft's offers a variety of learning options for its Data Science course, including:

    • Online courses that cover the basics of data science, including Python or R programming, statistics, and data visualization.
    • Foundational courses that provide an overview of data science and cover key concepts and techniques.
    • Hands-on, project-based courses that allow students to apply their knowledge to real-world problems and datasets.
    • Advanced courses that delve into more specialized topics, such as machine learning, deep learning, and natural language processing.

    Overall, the goal of the Edusoft's Data Science course is to provide a comprehensive education that equips students with the skills and knowledge they need to succeed in the field of data science.

    Edusoft's offers a variety of learning options for its Data Science course, including:

    Edusoft's's Data Science training in Lakshmi Nagar , Delhi provides multiple types of certifications. The certifications offered are:

    • ISO certification: This certifies that the training follows the high standards set by the International Organization for Standardization (ISO).
    • Skill India certification: This certifies that the training provides the skills required for the Indian job market.
    • NASSCOM certification: This certifies that the training aligns with the best practices in the Indian IT industry.

    These certifications provide credibility to the training and ensure that the students who complete the course are well-prepared for the job market. They also demonstrate that Edusoft's's training meets the highest standards of quality and professionalism.

    Yes! Many Data Science programs like Edusoft's's, offer some form of placement assistance, which can include:

    • Career counseling and guidance
    • Resume and cover letter assistance
    • Interview preparation and practice
    • Connections with potential employers and job opportunities
    • Networking events and career fairs

    Some programs also have relationships with specific companies that can provide internships or job opportunities for graduates. However, it is worth noting that while placement assistance can be helpful, it is not a guarantee of a job, and it is ultimately up to the individual to market themselves effectively and secure employment. Nevertheless, the placement assistance offered by Edusoft's can certainly give you a competitive edge in the job market.

    Absolutely, there are some differences in the teaching quality between the Pro and Pro-max programs offered by Edusoft's. The Pro program is designed for individuals who are new to the field of data science and provides a comprehensive introduction to the core concepts and techniques. The teaching quality is good, but it is geared towards beginners and may not be as in-depth or advanced as the Pro-max program. The Pro-max program, on the other hand, is designed for individuals who have some experience in data science and want to deepen their knowledge and skills. The teaching quality is typically higher, with more experienced instructors, more advanced course material, and more hands-on projects and real-world applications. Ultimately, the choice between the two programs depends on your prior experience and goals.

    Here are some of the most popular data science tools you can expect to learn in Edusoft's's Data Science courses:

    • Python and its data science libraries, such as Pandas, NumPy, Matplotlib, and Scikit-learn.
    • R programming language and its data science packages, such as ggplot2, dplyr, and tidyverse.
    • Big data technologies, such as Apache Spark, Hadoop, and MongoDB.
    • Data visualization tools, such as Tableau, Power BI, and D3.js.
    • Machine learning algorithms, such as linear regression, decision trees, and neural networks.

    In short, Edusoft's's Data Science courses will equip you with a well-rounded skillset to tackle various data science problems.

    Data scientists in India are also in high demand, and their salaries are quite impressive too. On average, data scientists in India can expect to earn between ₹9-15 lakhs per year (about $117,000 to $195,000 USD), depending on experience and location. As with the U.S., data scientists with advanced skills in machine learning and natural language processing tend to command higher salaries. Additionally, those working in industries such as finance, tech, and healthcare tend to earn more than those in other sectors. Overall, being a data scientist in India is a very promising career path financially!

    Edusoft's offers both live and pre-recorded data science course training sessions. Live sessions are great for interactive learning and real-time engagement with the instructor, while pre-recorded sessions offer flexibility and the ability to re-watch lectures at your own pace. The live sessions often include Q&A sessions and opportunities to interact with other students. The pre-recorded sessions are available 24/7 and can be accessed at any time, from any device. So, you can choose the option that best suits your learning style and schedule. It's like a choose-your-own-adventure learning experience!

    At Edusoft's, our instructors are experienced data scientists who bring real-world expertise to the classroom. They have a wealth of knowledge and experience in various industries, including finance, healthcare, marketing, and more. Our instructors are also passionate about teaching and dedicated to helping students succeed. They work closely with students to ensure they understand the material and are prepared for real-world applications. Our instructors are also active in the data science community, regularly participating in conferences, writing articles, and contributing to open-source projects. In short, our instructors are the best of the best, and they are committed to helping you succeed in your data science career.

    Absolutely! Investing in a data science course is a great way to get a return on your investment. Here's why:

    • Data science is a growing field, and there's a high demand for skilled professionals.
    • A data science course will provide you with a solid foundation in the field, including programming, statistics, and machine learning.
    • Having a data science certification on your resume can help you stand out in the job market.
    • Data science skills are transferable and can be applied in a variety of industries.
    • There are currently over 93,500 job openings for data scientists in India.
    • The average salary for a data scientist in India is INR 12 lakh per year.
    • The demand for skilled data scientists is only going to increase as more and more industries adopt data-driven approaches. With the right skills and experience, data scientists can command high salaries and enjoy a wide range of career opportunities in this fast-paced and exciting field. In short, investing in a data science course is a smart move that can pay off in the long run! It's like planting a money tree (if such a thing existed, of course).

    Here's the path to becoming a data scientist:

    • Get a bachelor's degree in a quantitative field like computer science, statistics, math, or engineering (not mandatory but gives a smooth start).
    • Pursue a master's degree or Ph.D. in data science or a related field (not required, but it helps).
    • Build a strong foundation in programming languages like Python, R, SQL, and statistical analysis techniques like regression, machine learning, and natural language processing.
    • Gain hands-on experience through internships, side projects, or freelance work.
    • Stay up-to-date with the latest technologies and techniques in the field through online courses, workshops, and conferences.
    • It takes dedication and hard work, but the rewards are worth it - a rewarding and lucrative career in one of the fastest-growing fields!

    The average salary for a data scientist in India is around INR 12 lakh per year (around $160,000 USD). However, this figure can vary greatly depending on a variety of factors, such as:

    • The size and location of the company
    • The level of experience and education of the data scientist
    • The specific skills and technologies the data scientist possesses
    • The demand for data scientists in the market at the time
    • Generally speaking, data scientists with more experience and a higher level of education can command higher salaries. Additionally, data scientists who specialize in specific technologies or industries, such as healthcare or finance, can also earn higher salaries. So, if you want to maximize your earning potential as a data scientist in India, it's important to stay current with the latest technologies and trends in the field.

    Most advanced data science and AI courses are typically self-paced, which means you can work through the material at your own speed and schedule. However, some programs offer instructor-led options as well, where you have access to an instructor or mentor who can provide guidance and support throughout the course. At Edusoft's we provide a mixture of both to give best of the best experience to our students. Here are some of the differences between self-paced and instructor-led courses:

    • Self-paced: Flexible schedule, independent learning, may lack interaction with instructor or peers.
    • Instructor-led: Structured schedule, direct guidance and feedback, more opportunities for collaboration and networking.
    • It really depends on your learning style and preferences. Some people thrive in a self-paced environment, while others prefer the structure and support of an instructor-led course. Whichever option you choose, you can be sure that you'll be gaining valuable skills and knowledge that can help you advance your career in data science or AI.

    Get ready for a firehose of knowledge! Data science and AI courses typically cover a wide range of topics, including:

    • Data wrangling: Cleaning, organizing, and preparing data for analysis
    • Exploratory data analysis: Summarizing, visualizing, and understanding data
    • Machine learning: Building algorithms to make predictions or find patterns in data
    • Statistical modeling: Using probability and statistical techniques to make predictions and decisions
    • Natural language processing: Analyzing and understanding text data
    • Deep learning: Advanced neural networks for pattern recognition and classification
    • And much more! Data science and AI are vast fields with endless opportunities for exploration and application. Buckle up, it's gonna be a wild ride!

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