What You'll Learn
Data Science Training in Maraimalainagar provides in-depth knowledge on data analysis, visualization, and machine learning techniques.
Learn how to preprocess and clean data effectively for accurate insights and predictive modeling in Data Science Course in Maraimalainagar.
Gain hands-on experience with Python, R, and popular data science libraries through real-world datasets.
Explore practical applications including business intelligence and customer analytics to drive data-driven decisions.
Master advanced algorithms for classification, regression, clustering, and recommendation systems.
Enhance your career with a focus on Data Science internships that provide real industry exposure in Maraimalainagar.
Data Science Course Objectives
- A BCA/B.Sc Statistics/B.Sc Mathematics/B.Sc Computer Science/B.Sc IT/BE or BTech or equivalent degree from a recognized university is required. He/she must have received a minimum of 50% on the qualifying examination.
The job opportunities available in this field are enthralling. Many young people aspire to make a career transition into these positions. Let's take a look at some of the major job positions in the Data Science domain.
- Data Analyst.
- Statistician Business.
- Analyst Database.
- Administrator.
- Data Engineer.
- Data Scientist.
- When it comes to becoming a data scientist, however, we notice that many professionals have dozens of MOOC courses and fancy buzzwords on their resumes or LinkedIn profiles. You can begin your data science career without any prior experience if you have the necessary knowledge.
- Math Skills: Learning Data Science techniques requires a strong understanding of mathematics. Your learning journey will be facilitated if you have strong mathematical skills.
- Statistics: Another prerequisite for learning Data Science is statistical knowledge. Having a solid understanding of inferential statistics and descriptive statistics would be a plus.
- Programming abilities: Being proficient in programming languages such as R, Python, SQL, and others are required to master Data Science.
- Data Visualisation abilities: Working with data visualization tools such as Tableau, Google Charts, Grafana, FusionCharts, and Datawrapper will help you gain expertise in Data science technology.
- There are numerous reasons why data science is one of the best career options in the twenty-first century. The demand and scope of this technology are the primary motivators for many people to pursue a career in this platform. Furthermore, the pay scale offered in this sector is piquing the interest of young people in becoming Data Scientists.
- The job roles available in this domain are recognized as among the highest paid in the country. As data science continues to expand its impact across multiple fields, it is expected to replace many existing job roles. As a result, for anyone looking for a stable career, data science is the best option.
- This Data Science domain claims to have one of the highest paying job roles in the country. A data scientist's annual salary in India is estimated to be 8.2 lakh. The annual salary ranges from 6 to 20 lakh rupees. There are approximately 40, 000+ existing job opportunities in India for various job roles in the domain of Data Science. Great Learning provides the best data science course in India, complete with job placements.
- Earning a bachelor's degree in data science or a related field is typically the first step toward becoming a data scientist, but there are other ways to learn data science skills, such as through a Bootcamp or the military. Before landing your first entry-level data scientist job, you should consider pursuing a specialization or certification in data science, as well as earning a master's degree in data science.
- Data scientists employ a wide range of skills, depending on the industry and job responsibilities. The majority of data scientists are familiar with programming languages like R and Python, as well as statistical analysis, data visualization, machine learning techniques, data cleaning, research, and data warehouses and structures.
- You will be able to gain expertise once you are familiar with the concepts and applications of the various Data Science tools and techniques. The only way to master Data Science and become a successful data science professional is through continuous learning and practice.
- If you are one of the many people who want to be a data scientist, you should be familiar with the various roles and responsibilities of a data scientist. What are the most important roles and responsibilities of a data scientist? Daily, what do data scientists do? Data Scientists play an important role in the organization's development.
- Many people believe that data scientists' responsibilities are limited to performing job roles such as data visualization, data processing, data munging, data mining, and so on. While these responsibilities are present, they do not present the entire picture.
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Phone (For Voice Call):
+91 89258 75257
WhatsApp (For Call & Chat):
+91 89258 75257
Benefits of Data Science Course
Data Science Certification Course in Maraimalainagar offers a rich and well-structured curriculum paired with valuable internship opportunities designed to provide extensive practical experience. Our program provides 100% Data Science course with placement assistance with dedicated career support, guiding you seamlessly towards successful career transitions in data science and analytics roles. By working on industry-relevant, real-world projects, you build a strong, impressive portfolio that effectively showcases your data science expertise and problem-solving capabilities. Participate in a hands-on Data Science internship in Maraimalainagar to gain real-time industry exposure and enhance your career readiness.
- Designation
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Annual SalaryHiring Companies
About Your Data Science Training
Our Data Science Course in Maraimalainagar Certification Course delivers cost-effective, in-depth learning of data analysis, machine learning, and statistical modeling. We connect you with a wide network of hiring partners to enhance job prospects. Gain practical skills through real-world Data Science projects in Maraimalainagar and case studies that prepare you for the competitive data science landscape. The Data Science fees are designed to offer great value for comprehensive training and certification.
Top Skills You Will Gain
- Data Wrangling
- Predictive Modeling
- Statistical Analysis
- Machine Learning
- Data Visualization
- Programming Skills
- Feature Engineering
- Data Mining
12+ Data Science Tools
Online Classroom Batches Preferred
No Interest Financing start at ₹ 5000 / month
Corporate Training
- Customized Learning
- Enterprise Grade Learning Management System (LMS)
- 24x7 Support
- Enterprise Grade Reporting
Why Data Science Course From Learnovita? 100% Money Back Guarantee
Data Science Course Curriculam
Trainers Profile
Our Data Science Course in Maraimalainagar is conducted by seasoned data scientists with strong expertise in machine learning, statistical modeling, and data visualization. They focus on practical training with real datasets and provide detailed data science training materials for a clear understanding of concepts. Learners are guided step-by-step to achieve their Data Science Certification while gaining hands-on industry skills. The trainers emphasize interactive sessions, personalized mentorship, and continuous support throughout the course. Additionally, students work on live projects that simulate real-world challenges, enhancing their problem-solving abilities and confidence.
Syllabus for Data Science Training 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.
Request more informations
Phone (For Voice Call):
+91 89258 75257
WhatsApp (For Call & Chat):
+91 89258 75257
Industry Projects
Career Support
Our Hiring Partner
Request more informations
Phone (For Voice Call):
+91 89258 75257
WhatsApp (For Call & Chat):
+91 89258 75257
Exam & Data Science 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 Maraimalainagar 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.
Our Student Successful Story
How are the Data Science Course with LearnoVita Different?
Feature
LearnoVita
Other Institutes
Affordable Fees
Competitive Pricing With Flexible Payment Options.
Higher Data Science Fees With Limited Payment Options.
Live Class From ( Industry Expert)
Well Experienced Trainer From a Relevant Field With Practical Data Science Training
Theoretical Class With Limited Practical
Updated Syllabus
Updated and Industry-relevant Data Science Course Curriculum With Hands-on Learning.
Outdated Curriculum With Limited Practical Training.
Hands-on projects
Real-world Data Science Projects With Live Case Studies and Collaboration With Companies.
Basic Projects With Limited Real-world Application.
Certification
Industry-recognized Data Science Certifications With Global Validity.
Basic Data Science Certifications With Limited Recognition.
Placement Support
Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.
Basic Placement Support
Industry Partnerships
Strong Ties With Top Tech Companies for Internships and Placements
No Partnerships, Limited Opportunities
Batch Size
Small Batch Sizes for Personalized Attention.
Large Batch Sizes With Limited Individual Focus.
Additional Features
Lifetime Access to Data Science Course Materials, Alumni Network, and Hackathons.
No Additional Features or Perks.
Training Support
Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.
Limited Mentor Support and No After-hours Assistance.
Data Science Course FAQ's
- LearnoVita is dedicated to assisting job seekers in seeking, connecting, and achieving success, while also ensuring employers are delighted with the ideal candidates.
- Upon successful completion of a career course with LearnoVita, you may qualify for job placement assistance. We offer 100% placement assistance and maintain strong relationships with over 650 top MNCs.
- Our Placement Cell aids students in securing interviews with major companies such as Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, Sports One , Infosys, MindTree, and MPhasis, among others.
- LearnoVita has a legendary reputation for placing students, as evidenced by our Placed Students' List on our website. Last year alone, over 5400 students were placed in India and globally.
- We conduct development sessions, including mock interviews and presentation skills training, to prepare students for challenging interview situations with confidence. With an 85% placement record, our Placement Cell continues to support you until you secure a position with a better MNC.
- Please visit your student's portal for free access to job openings, study materials, videos, recorded sections, and top MNC interview questions.
- Build a Powerful Resume for Career Success
- Get Trainer Tips to Clear Interviews
- Practice with Experts: Mock Interviews for Success
- Crack Interviews & Land Your Dream Job
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