Data Science Online Training Overview
Our Data Science course module is structured regarding a way to analyze huge Data using R programming and Hadoop. This online Data science certification course can revolve around the ideas of Python, Machine Learning, Data improvement, and Data Analysis. This Data Science course in Chennai will assist you to perceive core ideas and also the latest advancements as well as aspects of supervised, unattended, and also the latest and interesting field of Deep Learning by acting on many projects.
Data Science Online Training will:
- Learn about the most effective project management methodology for data processing - CRISP-DM at a high level.
- This course will make you associate degree skilled in Data Science ideas like Machine Learning, Algorithm, Regression, and Deep Learning.
- Learn SQL, Tableau, Java, Hive, Hadoop, Big Data, and stand out at the side of this expert Program.
- Practice the tools and techniques concerned in Data Science underneath the demand of industry experts.
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 Course Curriculam
LearnoVita is filled with the best and top MNC trainers +11 years of highly experienced professionals. As all Trainers are working professionals so they are having many live projects , trainers will use these projects during training sessions . Our trainer will give you technical supports and passionate about data and data-driven decision making.
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.
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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?
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Data Science Course Objectives
- In simple terms, data science is described as the process of gaining valuable insights from structured and unstructured data by using various accessories and techniques.
- Some of the techniques trained in data science include data extraction, data analysis, data mining, and data retrieval, to produce informative results.
- The character who is performing such varieties of jobs is called a data scientist. Moreover, it is widely used to make decisions and predictions through the use of authoritative analytics, machine learning, and predictive causal analytics.
- Data scientists are significant data wranglers. They necessitate an enormous mass of messy data points (unstructured and structured) and use their formidable talents in math, statistics, and programming to scrub, massage, and create them.
- Then they apply all their analytic powers – industry information, contextual understanding, skepticism of existing assumptions – to reveal hidden solutions to business objections.
- From my training experience, I would assert that the first and the foremost action in productively learning Data Science is to opt for an effective learning resource - the one that considers that learners are new to the field and are not well adept with the Data Science environment, the one that does not glide over the issues, the one that explains why the program is performing the way it is executing, the one that presents in-course assistance in the form of explaining the doubts the learners might have in a concept or while working practice problems.
- Well, I too acquired these the difficult way.
- Yes, data science careers are externally a doubt still a synonym for achievement.
- Here are the reasons why. Data science, like several other business-related phenomena, matches the fundamental laws of economics – supply, and demand. The demand for data science specialists is incredibly high, while the availability is just too low.
- Think about engineering years ago. The network was becoming a “thing” and other characters were making dangerous cash off it. Everybody wanted to enhance a programmer or an internet designer, or anything that might allow them to be inside the technology industry.
- Data scientists and data science are constantly improving and transfer to a tremendous degree completely the following ten years.
- We can surely assume that the Data Scientist will hold a ton of degrees following on and organizations seeking Data scientists will increment.
- The scope of data science In India includes associations in banking, social insurance, biotechnology, pharmaceuticals, media communications, web-based business, continuity, and automobile enterprises.
- "Data Scientist" is a neologism for a statistician who is 'computer literate'. But the computer literacy in this crisis is familiarity with new technologies that are important to a few new business models that have appeared over the past five years. It is very small or anything in this method that isn't well understood by practitioners of Business Intelligence, a discipline which is approximately 20-30 years old, depending against whom you require.
- The field is very crudely defined, both in terms of what duties a data scientist should be selected and in terms of what works a data scientist needs. “Data Scientist” means very complex things to several people (and companies). This is partially why you’ve begun to see titles like “Machine Learning Engineer” pop up because that’s slightly more precise.
- I think, like any other job, there are stressful features. I’ll give a good example of where “stress” can arise from in data science.
- Assume that you are accountable for creating a design that will try to predict which consumers are going to obtain their product.
- The company wants to use your image to avoid spending money buying to those customers who are not working to buy their product.
- This will protect their business because marketing to clients who will not purchase their product ultimately is a large consumption of cash.
- This is where the pressure comes in … what if your model is wrong? You have been given responsibility for how the corporation is going to employ its funds.
- One important part of information about being a data scientist- you have to read hard, all the time! In most other jobs, your variety of do your MBA or degree, and then quietly move on to doing a 'job' holding meetings, calls, some ppt displays, team coaching, administration, etc.
- The limit is that most of what you imagined is not used in your day-to-day job.
- In data science, on the other hand, each day and each project guide with them knowledge-related difficulties.
- The challenge might be in the coding or a different technique for a special data scenario or troubleshooting a bit of code that declines to work.
- A data science salary alone won’t make you rich according to Western models.
- You assumed you would be rich on $100k-$200k/year? Think repeatedly. While that’s good business, and while it would make you comfortable in a developing country, it’s still well within the bounds of normality in a first-world country.
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.