Data Science and Developer Training Overview
- Introduce you to the Data Science certification with R Programming training makes you an expert in data analytics using the R programming language.
- Learn Data Science, significance of Data Science in today’s digitally-driven world, components of the Data Science lifecycle, big data and Hadoop, Machine Learning and Deep Learning, R programming and R Studio, Data Exploration, Data Manipulation, Data Visualization, Logistic Regression, Decision Trees & Random Forest, Unsupervised learning, Association Rule Mining & Recommendation Engine, Time Series Analysis, Support Vector Machine - (SVM), Naïve Bayes, Text Mining, Case Study.
- Show you building a recommendation engine for an ecommerce site and will work on a real-time project.
- Data Science Certification Guidance Support with Exam Dumps
- Resume & Interviews Preparation Support
- We train students for interviews and offer placements in corporate companies.
Why Choose Data Science for Your Career
Data Science has become a revolutionary technology that everyone seems to talk about. Hailed as the ‘sexiest job of the 21st century’, Data Science is a buzzword with very few people knowing about the technology in its true sense. While many people wish to become Data Scientists, it is essential to weigh the pros and cons of data science and give out a real picture. In this article, we will discuss these points in detail and provide you with the necessary insights about Data Science
Advantages of Data Science
The various benefits of Data Science are as follows:
It’s in Demand
- Data Science is greatly in demand. Prospective job seekers have numerous opportunities. It is the fastest growing job on Linkedin and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.
Abundance of Positions
- There are very few people who have the required skill-set to become a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors. Therefore, Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.
A Highly Paid Career
- Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.
Data Science is Versatile
- There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.
Data Science Makes Data Better
- Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.
Data Scientists are Highly Prestigious
- Data Scientists allow companies to make smarter business decisions. Companies rely on Data Scientists and use their expertise to provide better results to their clients. This gives Data Scientists an important position in the company.
No More Boring Tasks
- Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.
Data Science Makes Products Smarter
- Data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences. For example, Recommendation Systems used by e-commerce websites provide personalized insights to users based on their historical purchases.
- This has enabled computers to understand human-behavior and make data-driven decisions.
Data Science can Save Lives
- Healthcare sector has been greatly improved because of Data Science. With the advent of machine learning, it has been made easier to detect early-stage tumors. Also, many other health-care industries are using Data Science to help their clients.
Data Science Can Make You A Better Person
- Data Science will not only give you a great career but will also help you in personal growth. You will be able to have a problem-solving attitude. Since many Data Science roles bridge IT and Management, you will be able to enjoy the best of both worlds
Skills You Will Gain
- R Programmming
- Python, SAS
- Artifical Intelligence
- Deep Learning & Machine Learning
- Statistics, Naive Bayes
- Programming, Neural Networks
- Data Mining, Visualization
Data Science Course Key Features 100% Money Back Guarantee
5 Weeks TrainingFor Become a Expert
Certificate of TrainingFrom Industry Data Science Experts
Beginner FriendlyNo Prior Knowledge Required
Build 3+ ProjectsFor Hands-on Practices
Lifetime AccessTo Self-placed Learning
Placement AssistanceTo Build Your Career
Top Companies Placement
- Annual SalaryHiring Companies
Data Science Certification Course Curriculam
Syllabus of Data Science Course in Chennai Download syllabus
- What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
- Hands-on Exercise - Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
- Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.
- Hands-on Exercise -Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
- Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.
- Hands-on Exercise -Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
- Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.
- Hands-on Exercise -Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
- Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
- Hands-on Exercise -– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
- Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
- Hands-on Exercise -Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
- Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression
- Hands-on Exercise -Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
- What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.
- Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
- What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.
- Hands-on Exercise -Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data
- Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.
- Hands-on Exercise -Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
- Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
- What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
- Hands-on Exercise -Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.
- Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
- What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
- Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
- This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
+91 909 279 9991
Request for Information
Wallmart Sales Data Set
Retail is another industry that extensively uses analytics to optimize business processes.
Flipkart Classification Dataset
This project is to forecast sales for each department and increasing labelled dataset using semi-supervised classification.
Credit Card Fraud Detection
The project consist of data analysis for various parameters of banking dataset and data visualization for finding the probability of occurrence of fraudulent activities.
- Mock interviews by Learnovita give you the platform to prepare, practice and experience the real-life job interview. Familiarizing yourself with the interview environment beforehand in a relaxed and stress-free environment gives you an edge over your peers.
- In our mock interviews will be conducted by industry best Data Science Course in Chennai experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
How Learnovita Mock Interview Works?
Request for Information
Data Science Course Objectives
- Data science has emerged as an attractive career option for freshers as well as experienced professionals. The demand for data engineers is very high in sectors like information technology, telecom, manufacturing, finance and insurance, retail and many more
- Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option. Start Learning Your Advance Data Science Course in Chennai
- Career progression opportunities for individuals who become Data Science are excellent
- With IT skills in strong demand, deciding to become a Data Science can open opportunities to progress your career in both the private and public sectors.
- Data Science Salary in worldwide is the most lucrative in all the field of computer, Internet networking industry and More.
- Certainly Yes. Even the recruiters know the knowledge we get in colleges is not enough to do a software job. They will see how confident you are. However they will train you according to their requirement once you get into the company.
- Be confident with your basics that's enough You to get tons of Jobs opportunity by learning Data Science from Data Science Coaching Centers in Chennai.
- Deep practical knowledge, Hands-on lab & Data Science Course with placement in Chennai.
- Real-time project use cases & scenarios from the various Industries.
- Mock Tests and discussing various questions.
- Data Science Course with Placement in Chennai has been actively involved in 100% Job Placement Assistance as a value-added service in the Technical Program. With the backup of an advanced training curriculum and real-time business projects, we have a very consistent and growing Job Placement and Track Record.
- Market entry to various countries and jobs in major corporate.
- Immediate job opportunities after Completion of Advance Data Science Course in Chennai.
- Active Coordination with students from the stage of preparing a professional CV/Resume to attend Interviews and securing a Job.
- Preliminary Preparation ensures that our students are able to perform confidently in Interviews even it was their First Interview.
- There are no prerequisites for taking up this training course. If you like mathematics, you can accelerate your learning through this Data Scientist course.
- Mostly Yes, Having Agile Thinking is enough to Understand the Data Science Concepts. Even though The Basic mathematics & Programing Language knowledge is to make Ease to Understand the Concepts Easily.
- Introduction to Data Science and its importance
- Data Science life cycle and data acquisition
- Experimentation, evaluation, and project deployment tools
- Various Machine Learning algorithms
- Predictive analytics and segmentation using clustering
- Fundamentals of Big Data Hadoop
- Roles and responsibilities of a Data Scientist
- Using real-world datasets to deploy recommender systems
- Working on data mining and data manipulation
The Data Science Course is perfect for the below job positions:
- Information Architects and Statisticians
- Developers looking to master Machine Learning and Predictive Analytics
- Big Data, Business Analyst, and Business Intelligence professionals
- Aspirants looking to work as Machine Learning experts, Data Scientists, etc.
- The demand for Data Scientists far exceeds the supply. This is a serious problem in a data-driven world that we are living in today. As a result, most organizations are willing to pay high salaries for professionals with appropriate Data Science skills.
- Data science Course will help you become proficient in Data Science, R programming language, Data Analysis, Big Data, and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.
Online Classroom Batches Preferred
(Class 1Hr - 1:30Hrs) / Per Session
(Class 1Hr - 1:30Hrs) / Per Session
(Class 3hr - 3:30Hrs) / Per Session
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Exam & Certification
At LearnoVita, You Can Enroll in Either the instructor-led Data Science Online Course, Classroom Training or Online Self-Paced Training.
Data Science Online Training / Class Room:
- Participate and Complete One batch of Data Science Course Course
- Successful completion and evaluation of any one of the given projects
Data Science Online Self-learning:
- Complete 85% of the Data Science Certification Training
- Successful completion and evaluation of any one of the given projects
These are the Different Kinds of Certification levels that was Structured under the Data Science Certification Path.
- Certified Analytics Professional (CAP)
- Cloudera Certified Associate: Data Analyst
- Cloudera Certified Professional: CCP Data Engineer
- Data Science Council of America (DASCA) Senior Data Scientist (SDS)
- Data Science Council of America (DASCA) Principle Data Scientist (PDS)
- Dell EMC Data Science Track
- Google Certified Professional Data Engineer
- Google Data and Machine Learning
- IBM Data Science Professional Certificate
- Microsoft MCSE: Data Management and Analytics
- Microsoft Certified Azure Data Scientist Associate
- Open Certified Data Scientist (Open CDS)
- SAS Certified Advanced Analytics Professional
- SAS Certified Big Data Professional
- SAS Certified Data Scientist
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LernoVita Data Science Certification Training in Chennai That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
Pranav SrinivasSoftware Testing, Capgemini
Data Science Course FAQ's
- LearnoVita Best Data Science Course in Chennai will assist the job seekers to Seek, Connect & Succeed and delight the employers with the perfect candidates.
- On Successfully Completing a Career Course from LearnoVita Best Data Science Course in Chennai, you Could be Eligible for Job Placement Assistance.
- 100% Placement Assistance* - We have strong relationship with over 650+ Top MNCs, When a student completes his/ her course successfully, LearnoVita Placement Cell helps him/ her interview with Major Companies like Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, HCL, Infosys, MindTree and MPhasis etc...
- LearnoVita is the Legend in offering placement to the students. Please visit our Placed Students's List on our website.
- More than 5400+ students placed in last year in India & Globally.
- LearnoVita is the Best Data Science Course Institute in Chennai Offers mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
- 85% percent placement record
- Our Placement Cell support you till you get placed in better MNC
- Please Visit Your Student's Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
- LearnoVita Certification is Accredited by all major Global Companies around the World.
- LearnoVita is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of Data Science and National Institute of Education (nie) Singapore
- Also, LearnoVita Technical Experts Help's People Who Want to Clear the National Authorized Certificate in Specialized IT Domain.
- LearnoVita is offering you the most updated Data Science certification training in Chennai, relevant, and high-value real-world projects as part of the training program.
- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
- You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc.
- After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
- We will reschedule the Data Science classes in Chennai as per your convenience within the stipulated course duration with all such possibilities.
- View the class presentation and recordings that are available for online viewing.
- You can attend the missed session, in any other live batch.