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
Data Science Training Overview
This Data Science Certification Course Training offers comprehensive learning through self-paced videos and live instructor sessions that help you develop skills in the shortest time possible. Candidates from various technological or quantitative backgrounds, such as engineering, economics, mathematics, statistics, business management, wish to begin their career in data science with Professional Data Science Certificate. It also recommended that candidates with non-technical background knowledge of the basics of data analytics tools such as Excel, SQL, Tableau etc.
Data Science Training will:
- Gain a detailed understanding of the structure of data and manipulation of data.
- With any company betting heavily on data science to produce more market value, the demand for data scientists has skyrocketed.
- Data science is a combination of maths, statistics, and programming that is capable of being more cutting edge than this.
- To take proactive business decisions, conduct forecasting.
- To reflect data for easy comprehension, use Data Principles.
Top Skills Covered
- R Programmming Python
- SAS Artifical Intelligence
- Deep Learning
- Machine Learning Statistics
- Naive Bayes
- Linear Algebra
- Neural Networks Data Mining
Data Science Learning 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
Best Profession Placement Assistance
The Data Scientist helps to design the data modelling process to create algorithms. They build the predictive models and perform the custom analysis to combine the models through ensemble modelling, Identify the valuable data resources and automate the collecting process to analyze and discover the trends and patterns and rewarded with substantial pay raises shown below.
Annual SalaryHiring Companies
Data Science Certification Course Content
Trainers are certified professionals with 13+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Data Science Online Training course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
It is not always necessary for professionals to have a data science background wheeling beforehand.
You might be a student or a fresher who is developing an interest in the data science field and planning to get individual experience in the sector. Or you might be a professional who is already established in one industry but wants to enter the data science course because of the love for data or the rising interest and demand the profile offers.
Syllabus of Data Science Online Course 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.
- 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. .
Employee Management System (EMS)
Create a new system to automate the regulation creation and closure process.
- 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.
- Our mock interviews will be conducted by industry 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 Training Objectives
- Let's always be things straight: to even get a job in data science, you should not need a data science credential.
- Your data preparation should be selected based on its skills rather than on a credential because hiring managers are not really interested in any certification for data sciences.
- While it took 2 to3 years mostly to teach you all of this in undergraduates and Masters courses at educational institutions, many claims that you can acquire them by spending 6 to 7 hours a day in just six months.
- Their is No surprise that skilled data scientists in occupations worldwide are well-rewarded.
- However, my ideas definitely can help to enhance your capacity, earn a good side revenue as a business analyst, and become your own boss most especially.
- What is a junior business analyst doing in the U.S.
- The average Senior Data Scientist wage in the US is $86,315, but usually, the wage level ranges from $76,996 to $96,200.
- In any business, both software engineers and project managers play a key role.
- In comparison with data scientists, data development does not attract the same media coverage, but the average salary appears to be larger than the maximum management consultant: (data scientist).
- DSI participants come from different backgrounds but have a similar task: to start a career in information science or technical analysis they are enthusiastic.
- Our career transition team includes engineers, new graduates, mid-career production and financial analysts and place of business, and others from a wide range of fields such as advertising and law. we are aware of technical changes.
A Highly Paid Profession
- One of the highest-paid workers is the data center.
- Data scientists earn an average of $116,100 a year, and according to Glassdoor.
- Data Science is therefore a very attractive career choice.
- It would be difficult for people with very few weeks of practice to get a job.
- There are too many people who call themselves data scientists nowadays, who generally describe themselves as enthusiasts of the data science and have no experience that only a few applicants can get a career.
How to originate your data science profession :
- Step 0: Identity what you need to know.
- Step 1: Get Python in comfort.
- Step 2: Learn Pandas Statistical Analysis, handling, and viewing.
- Step 3: Learn scientist-learn machine learning.
- Step 4: Comprise more breadth of machine learning.
- Step 5: Continue to study and practice.
- Data scientists are responsible for doing what data engineers can do in certain organizations.
- Although data scientists are not capable of being data engineers, they may obtain the know-how.
- But in the other extreme, if data engineers start to do data science, it is much less popular.
Data Science Exam & Certification
At LearnoVita, You Can Enroll in Either the instructor-led Online Classroom Training or Online Self-Paced Training.Online Classroom:
- Participate and Complete One batch of Data Science Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Data Science Certification course
- Successful completion and evaluation of any one of the given projects
Honestly Yes, We Provide 1 Set of Practice test as part of Your Data Science Training course. It helps you to prepare for the actual Data Science Certification exam. You can try this free Data Science Fundamentals Practice Test to Understand the Various type of tests that are Comes Under the Parts of Course Curriculum at LearnoVita.
- Certified Analytics Professional (CAP)
- Cloudera Certified Associate (CCA)
- Cloudera Certified Professional (CCP)
- Data Science Council of America (DASCA)
- Data Science Council of America (DASCA)
- 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 LearnoVita Online Training Course 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.
Swetha SinghBusiness Analysts
Data Science Course FAQ's
- LearnoVita will assist the job seekers to Seek, Connect & Succeed and delight the employers with the perfect candidates.
- On Successfully Completing a Career Course with LearnoVita, 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 Conducts development sessions including 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 AWS 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, 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 classes 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.