Data Science Training

Master Data Science:
Learn the skills needed to solve complex data problems

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Data Science Training

DataScience Training

Overview of Data Science Course

Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.

Necessary Required Skills

1. Intro to Data & Data Science

We live in a data-driven world & studies evaluate that the data gets generated continuously across the globe. With the increasing demand, analyzing the data and its principles help you advance in your career in data science. Data science aid firms to find patterns in massive volumes of organized & unstructured massive data. As a result, businesses may enhance efficiency, control expenses, uncover new market possibilities, and strengthen their competitive edge.

You will learn: Our Data Science training will help you gain an in-depth understanding of Big Data, Business Intelligence, Business Analytics, Machine Learning & Artificial Intelligence concepts. Our instructors will give you a comprehensive knowledge of these concepts & help you fit in the domain.

2. Mathematics

Before you delve into any specific techniques, it is crucial to have a solid foundation in calculus & linear algebra, which will allow you to understand and use the fundamental mathematics concepts of the field.

You will learn:Our Data Science course will show you how to use derivatives, integrals & matrices to solve problems related to data science.

3. Statistics

Statistics trains your mind to frame problems as hypotheses & gives you techniques to test these hypotheses, just like a scientist.

You will learn:Our data science training prepares you to think like an expert by utilizing various statistical techniques alongside improving your skills. You not only acquire the skills you need, but you also learn how to apply them in this domain.

4. Python

Python is a relatively new programming language & unlike R, it is a general-purpose programming language. Web applications, computer games & data science are many of its capabilities. Extreme libraries enable data manipulation, transformation & visualization. Also, Python deals with machine learning & deep learning.

You will learn: Trainees will learn powerful frameworks such as sci-kit-learn, TensorFlow & more, which they will use to develop, implement, and deploy machine learning models.

5. Advanced Statistics

Statistical methods such as regressions, clustering & factor analysis can be performed through machine learning to produce predictions with unparalleled accuracy. This section will go through these strategies in depth.

You will learn: Predictive modeling is a crucial component of data science & you can hone your skills with advanced statistics. Our curriculum allows trainees to integrate data into projects using these concepts.

6. Machine Learning

The final part of the program will focus on deep learning, the most recent advancement in machine-learning technology.

You will learn: Through Machine Learning, learn how to use deep learning with TensorFlow to take your data analysis to the next level.

  • Our data science training course offers advanced modules to become a professional data scientist.
  • Fill your portfolio with in-demand data science abilities such as statistical analysis, Python programming with NumPy, pandas, matplotlib, Seaborn & data visualization.
  • Demonstrate your knowledge in the data science domain to impress various interviewers.
  • Learn how to pre-process data
  • Understand the mathematics behind machine learning.
  • Start coding in Python & learn how to use it for statistical analysis.
  • Perform linear & logistic regressions in Python.
  • Carry out the cluster & factor analysis
  • Be able to create machine learning algorithms in Python using NumPy, stats models & sci-kit-learn.
  • Apply your skills to real-life business cases.
  • Use cutting-edge deep learning frameworks such as Google's TensorFlow to develop business experience while coding & tackling Big Data issues.
  • Untap the potential of deep neural networks.
  • Study underfitting, overfitting, training, validation, n-fold cross-validation & testing to improve Machine Learning algorithms. Learn how hyperparameters may help you perform much better.
  • Warm up your fingers as you will be eager to apply everything you have learned here to more & more real-life situations.
  • Problem-Identification
  • Obtaining the correct data
  • Data purification
  • A scarcity of professionals
  • Identifying the problem
  • Data accuracy
  • Quantity of data
  • Several data source
  • Inadequate domain knowledge
  • Communication of results
  • Data protection

Each module in our course is created uniquely by our experts. As a result, we introduced the Data Science course to offer the most effective, time-efficient, and organized Data Science training virtual. Moreover, our focus is to teach topics that flow smoothly & complement each other. The curriculum teaches you everything you need to know about this technology. Data science is a field that encompasses several facets that covers a wide range of topics.

  • Knowing the domain & the types of analyses performed
  • Mathematics
  • Statistics
  • Python
  • Applying advanced statistical techniques in Python
  • Data Visualization
  • Machine Learning
  • Deep Learning

EduXfactor offers Data Science training that helps individuals master the skills & competencies associated with this career. The following are the unique features covered in this course.

  • Demo Training
  • Virtual Training & Offline Training
  • Flexible Batches
  • Professional Curriculum
  • Internship on Real-Time Projects
  • Industry Expert Faculties
  • 100% Job Oriented Training
  • Course Completion Certification
  • Industry Success Case Studies
  • Life Time Video Access
  • Resume Building & Placement Assistance
  • Gain Hands-on Experience
  • Mock Interviews

Listed below are some of the leading data science careers you can break into after completing the data science course.

  • Our mentors provide an opportunity to every individual, aspirant & working professionals to evaluate the subject and learn more through constant models.
  • Our trainers help the individuals to finish their undertakings by setting them up for demands, questions & replies. Individuals are allowed to address any aspects whenever possible.
  • We provide a unique approach to offline/virtual learning. This approach enables you to draw on your own experience in acquiring & processing knowledge.
  • The instructors will provide data & assign work that will simulate real-world scenarios.
  • Our guides are industry-oriented, trained professionals and subject specialists who run applications that give the best data science training to the aspirant & professionals.

Virtual/ Offline Training

- EduXfactor offers offline/virtual training based on trainee requirements with a personalized curriculum, as our training duration is for (45 – 50) days. Our professional expert training helps you reach your career & enhance your skill on your particular course.

Internships & Training

- EduXfactor offers data science training & internships to working professionals, aspirants under the guidance of professional experts. The program lasts for three to four months. Individuals might opt to work full-time or part-time from home or an in-house office. We offer the training program to individuals who want to sharpen their skills.

Data science is all about gathering, preparing, evaluating & visualizing data. It is a study about data that includes designing the methods of recording, storing data & then effectively extracting the needed information. Handling a large amount of data in modern business data science has become a vital part of IT. Organizations may utilize scientific approaches to execute tests and extract findings to give clear insight information about their prospective customers.

Enhances Your Career in Data Science: First, consider the suitable position you would like to work as a data scientist. It has a wide range of applications for data scientists, from machine learning engineers to enterprise architects. The following are the most common data science career pathways.

Data Analyst: The Data Analysts are entry-level data scientists who are just starting their careers. They analyze the data & write the recommendation to create their technical programs to solve the problems. It revolves around using the business data to generate actionable insights in C-suite.

Data Scientist: As Data scientists, experts develop a solution for critical problems & create programs to review data. They also create machine learning models for prediction, identifying patterns & trends in data with enterprise artifacts using technologies such as SQL, R, Hadoop, MongoDB, Tableau & more.

Data Engineer: Professional & experienced data engineers are qualified to develop ways for interpreting data at different scales. They are responsible for producing data pipelines & improving the flow of information. Data engineers will have technical skills in data preparation, statistics, data wrangling/munging, data visualization & programming languages such as Python, R programming, SQL, Java, and others.

Pay Range or Data Science: This domain is in great demand among e-commerce businesses & even start-ups. India is the second-highest range for recruiting various employment positions in this domain.

Course Curriculum

  • Recap of Demo
  • Introduction to Types of Analytics
  • Project life cycle
  • An introduction to our E learning platform
  • Understanding Business problems and Data understanding
  • Data cleaning, data preparation, EDA, Feature Engineering
  • Data Mining
  • Model Evaluation
  • Model Deployment
  • Monitoring & Maintenance
  • Topics
  • Data Types
  • Measure Of central tendency
  • Measures of Dispersion
  • Graphical Techniques
  • Skewness & Kurtosis
  • Box Plot
  • R language
  • R Studio
  • Descriptive Stats in R
  • Python Programming (Installation & basic commands) Libraries
  • Jupyter notebook
  • Set up Github
  • Descriptive Stats in Python
  • Pandas & Matplotlib / Seaborn
  • Topics
  • Random Variable
  • Probability
  • Probability Distribution
  • Normal Distribution
  • Standard Normal Distribution (SND)
  • Expected Value
  • Sampling Funnel
  • Sampling Variation
  • Central Limit Theorem (CLT)
  • Confidence interval
  • Assignments Session
  • Introduction to Hypothesis Testing
  • Hypothesis Testing with examples
  • Proportion tests
  • Sample t-test
  • Anova & Chisquare case studies
  • Visualization
  • Data Cleaning
  • Imputation Techniques
  • Data Visualizing Plots
  • Correlation analysis
  • Transformations
  • Normalization & Standardization
  • Topics
  • Encoding Methods
  • One Hot Encoding (OHE)
  • Label Encoders
  • Mean Frequency Encoding
  • Mean Encoding
  • Frequency Encoding
  • Outlier Detection
  • Predictive Power Score
  • Topics
  • Recursive Feature Elimination
  • Principle Component Analysis (PCA)
  • Latent Dimension Analysis (LDA)
  • Topics
  • Splitting data into train & test
  • Methods of cross-validation
  • Accuracy methods
  • Classification models
  • Shallow machine learning models
  • Ensemble models
  • Regression models
  • Blackbox techniques
  • Topics
  • Principles of Regression
  • Introduction to Simple Linear Regression
  • Multiple Linear Regression
  • MSE (mean squared error)
  • RMSE (root mean squared error)
  • R2score
  • Adjusted R2score
  • Topics
  • Principles of Logistic Regression
  • Introduction to Logistic Regression
  • Logistic Regression
  • Confusion matrix
  • False Positive, False Negative
  • True Positive, True Negative
  • Sensitivity, Recall, Specificity, F1 score
  • Receiver operating characteristics curve (ROC curve)
  • Lasso Regression
  • Ridge Regression
  • Topics
  • Deciding the K value
  • Split the data and build the KNN model.
  • Understanding the various generalization, regulation techniques to avoid overfitting and underfitting
  • Kernel tricks
  • Topics
  • Classification Tree Elements - Root node, Child Node, Leaf Node & more.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information Gain
  • Implementation of a Decision tree using C5.0 & Scikit learn libraries
  • Topics
  • Probability – Recap
  • Bayes Rule
  • Naive Bayes Classifier
  • Text Classification using Naive Bayes
  • Topics
  • Bagging
  • Boosting
  • Random Forest
  • XGBM
  • LGBM
  • Topics
  • Data Mining Process
  • Hierarchical Clustering / Agglomerative Clustering
  • Measure of distance
  • Numeric - Euclidean, Manhattan, Mahalanobis
  • Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient & more.
  • Mixed - Gower’s General Dissimilarity Coefficient
  • Types of Linkages
  • Single Linkage / Nearest Neighbour
  • Complete Linkage / Farthest Neighbour
  • Visualization of clustering algorithm using Dendrogram
  • Topics
  • R shiny
  • Streamlit
  • Topics
  • PCA
  • Why dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix algebra
  • Topics
  • What is Market Basket / Affinity Analysis?
  • A measure of association rules
  • Support
  • Confidence
  • Lift Ratio
  • Apriori Algorithm
  • User-based collaborative filtering
  • The measure of distance/similarity between users
  • Driver for recommendation
  • Computation reduction techniques
  • Search based methods to item collaborative filtering
  • Vulnerability of recommender systems
  • Topics
  • Sentiment Extraction
  • Lexicons & Emotion Mining
  • Topics
  • Artificial Neural Network
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Activation function
  • Network Topology
  • Classification Hyperplanes
  • Best fit “boundary”
  • Gradient Descent
  • Stochastic Gradient Descent Intro
  • Back Propagation
  • Introduction to concepts of CNN
  • Topics
  • Introduction to time series data
  • Steps of forecasting
  • Components of time series data
  • Scatter plot & Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naive forecast methods
  • Forecast errors and its metrics errors
  • Model-Based approaches
  • Linear Model
  • Exponential Model
  • Quadratic Model
  • Additive Seasonality
  • Multiplicative Seasonality
  • Model-Based approaches
  • AR (Auto-Regressive) model for errors
  • Random walk
  • ARMA (Auto-Regressive Moving Average), Order p & q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d & q
  • A data-driven approach to forecasting
  • Smoothing techniques
  • Moving Average
  • Simple Exponential Smoothing
  • Holts / Double Exponential Smoothing
  • Winters / HoltWinters
  • De-seasoning and de-trending
  • Forecasting using Python & R

Upcoming Batches

Type Course Name Start Date Time Day

Frequently Asked Questions (FAQ)

The demand for a data scientist is rapidly growing globally. Data Science, Machine Learning, and other fields are incredibly promising, interesting & have limitless applications. Even though there are many practitioners, there is a lack of skilled specialists in these domains. Data science skill is in great demand since it results in tangible & measurable perks. The significant increase in open data science professions indicates that data science careers are well-positioned for the future.

Statistics from numerous employment portals reveal that the number of data science jobs posted has steadily increased over the years.

There are several paths to becoming a data scientist. The following are the primary steps to pursuing a career in data science.

  • - Choose a profession in data science.
  • - Continue to learn by developing your data science projects.
  • - Learn about data science using our personalized data science training, such as providing resources, unique programs & more.
  • - Build your data science portfolio while improving & refining your abilities through our expertise.

For many years, one of the top occupations in India was that of a data scientist. In terms of compensation, job demand, work satisfaction, organizations utilize the phrase "data scientist" to refer to other comparable professions, such as "data analyst."

Demand for data science professionals is rapidly increasing in the market, as the organization maintains itself through data-driven insights. Various firms notice the worth & potential of Big Data knowledge as they thrive to use it to create higher business choices. The supply of skilled applicants is growing at a higher pace. So, it is a great choice to become a data scientist to become a stalwart in the IT industry.

We are happy & proud to say that we have impaneled with numerous small, mid-sized MNCs. Many of these organizations/businesses have data science opportunities widely open for the skillful. Furthermore, we have highly active placement support that assists our students with 100% placement. Our support team will also help by training participants through mock interviews and other skill enhancement training.

The demand for a data scientist is rapidly growing globally. Because the requirement in this domain is so high & the supply of people who can do this job well is limited, data science offers huge pay and perks in a position even at entry-level. Several organizations/companies also refer to data analysts as data scientists. Particular individuals are usually involved in operating with the organization's database.

So coming to this, data science is the field that comprises everything related to data cleansing, data mining, data preparation & data analysis. Big data refers to the amount of data that is difficult to store & process in real-time. These data utilize insights that can lead to better decision-making. Data science algorithms will create a difference with the data insights across industries (like internet searches, search recommendations & advertisements).

Data analysts do not require significant coding abilities, but they should be familiar with analytics tools, data visualization software & data management applications.

Our personalized curriculum is programmed to give a hands-on approach to the students in Data Science. The data science course program teaches the basics of every module, followed by high-intensity practical sessions reflecting the current challenges & needs of the industry that will demand aspirants time & commitment.


  • 30 Videos
  • 45 Lectures
  • Exp. 5 Year
4.2 out of 5.0
5 Star 85%
4 Star 75%
3 Star 53%
1 Star 20%

Item Reviews - 3

Pooja Rani


"I have become a fan of the course trainer. He is helpful, professional, and knowledgeable, doing his best in clearing all my doubts. I can gladly say that learning from EduXFactor is worth your time and money! "

Mubashshir Ali


" Whether you want to learn AI, machine learning, or data science, EduXFactor is simply the best platform for data-based courses. This data science course's presentation is really commendable. After completing this course, I've been able to switch from a Hadoop Developer to a Big Data Engineer with a good salary hike. "

Ankit Kumar Patel


" I loved the curated study materials that you get access to after joining this course. The projects and assignments were based on real-life and had a great balance of challenge and fun. Whatever the questions I raised were also resolved quickly, helping me to complete the course in time. "

S Naveen Sunny


" Hands down, EduXFactor is the best platform for e-learning. I started here with a digital marketing course but then decided to try data-based courses to expand my skillset. This course is top-notch, and the instructors are not only qualified and knowledgeable but also extend complete help whenever asked for. "

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Datascience Training
Course Features
  • Demo Training
  • Virtual Training & Offline Training
  • Flexible Batches
  • Professional Curriculum
  • Internship On Real-Time Projects
  • Industry Expert Faculties
  • 100% Job Oriented Training
  • Certification Guidance
  • Industry Success Case Studies
  • Life Time Video Access
  • Resume Building & Placement Assistance
  • Gain Hands-on Experience
  • Mock Interviews

Course Features

  • Student Enrolled:1740
  • lectures:10
  • Quizzes:4
  • Duration:60 hours
  • Skill Level:Beginner
  • Language:English
  • Assessment:Yes

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