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Data Science Course Online

(4.6) 9665 Ratings 10987Learners
100% Job Guarantee | Minimum CTC: ₹ 6 LPA

Learn statistical modeling, machine learning, and data analysis with our Data Science certification course. Get proficient with Python, R, and essential tools through practical applications under the guidance of industry professionals. Our extensive curriculum guarantees that you are prepared for today’s data-driven problems, advancing your career as a highly sought-after data scientist.

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  • 40+ Hrs Hands On Learning
  • 3+ Live Best Industry Projects
  • 25+ Practical Assignments
  • 24/7 Student Helpdesk

Online Classroom Batches Preferred

20- May- 2024
Monday (Monday - Friday)

Weekdays Regular

08:00 AM (IST)

(Class 1Hr - 1:30Hrs) / Per Session

15- May- 2024
Wednesday (Monday - Friday)

Weekdays Regular

08:00 AM (IST)

(Class 1Hr - 1:30Hrs) / Per Session

18- May- 2024
Saturday (Saturday - Sunday)

Weekend Regular

11:00 AM (IST)

(Class 3hr - 3:30Hrs) / Per Session

18- May- 2024
Saturday (Saturday - Sunday)

Weekend Fasttrack

11:00 AM (IST)

(Class 4:30Hr - 5:00Hrs) / Per Session

Can't find a batch you were looking for?
₹21000 ₹16000 10% OFF Expires in

No Interest Financing start at ₹ 5000 / month

A Comprehensive Overview of Data Science

Data science combines domain knowledge, computer science, statistics, and mathematics to extract insights from large datasets, revolutionizing decision-making and commercial processes. The careful gathering and preparation of data using programs like SQL, Python, and R is essential to this field—analytical methods, including descriptive, diagnostic, and predictive analytics, fuel innovation and optimizations. Beyond the commercial world, data science is used in government, education, and healthcare to improve patient care, tailor instruction, and shape policy. Ethical concerns about bias and data privacy emphasize the conscientious use of data. Essentially, data science uses data analysis and utilization to alter business, technology, and society. Explore these realms and more with our Data Science course with placement.

Additional Info

Exploring the Future Trends of Data Science

  • Artificial Intelligence and Machine Learning: With growing use across sectors, AI and ML will continue to be essential components of data science. More complex data analysis and predictive modeling will be possible because of developments in natural language processing, reinforcement learning, and deep learning algorithms.
  • Big Data and Real-Time Analytics: To process and analyze data quickly as it continues to expand rapidly, data scientists will need to make use of big data technology and real-time analytics platforms. Technologies for stream processing, as well as distributed computing frameworks like Apache Spark and Hadoop, will be used in this.
  • Data from the Internet of Things (IoT): As Internet of Things devices proliferate and generate enormous volumes of data, there will be a bigger focus on deriving useful insights from sensor data. IoT data streams will be subjected to data science approaches to enhance decision-making across a range of industries, including manufacturing, healthcare, and smart cities, monitor and optimise operations, and anticipate equipment breakdowns.
  • Explainable AI and Ethical AI: Creating AI models that are clear, understandable, and devoid of prejudices will become increasingly important. Ethical AI frameworks guarantee that AI applications follow ethical standards and rules, while explainable AI methodologies will allow data scientists to comprehend and evaluate AI systems' judgments.
  • Automated Machine Learning (AutoML): Model selection, feature engineering, and hyperparameter tuning may all be automated with AutoML tools and platforms, which will help democratize machine learning. This will make it easier for non-experts to create and implement machine learning models, hence lowering the entrance barrier for data science.
  • Edge Computing and Edge AI: By allowing data processing and analysis to be carried out closer to the location where the data is generated, edge computing will lower latency and bandwidth needs. This trend will coincide with the emergence of Edge AI, which applies machine learning models directly to edge devices like sensors, cameras, and smartphones.
  • Privacy-Preserving Data Analysis: Methods for privacy-preserving data analysis will be scrutinized more as worries about data security and privacy grow. Thanks to federated learning, homomorphic encryption, and differential privacy, data scientists will be able to analyze sensitive data while maintaining individual privacy.

Tools and Technologies for Data Science

Programming Languages:

  • Python: Because of its ease of use and many libraries, including NumPy, Pandas, Matplotlib, and Scikit-learn, Python is often considered the language of choice for data science.
  • R: Another well-liked programming language in data science, particularly for data visualization and statistical analysis.

Integrated Development Environments (IDEs):

  • Jupyter Notebook: An interactive environment that supports code execution in an understandable and modular style. It is perfect for data exploration, visualization, and analysis.
  • Rstudio: Sophisticated IDE with features for code editing, debugging, and visualization that was created especially for R programming.

Tools for Data Visualisation:

  • Matplotlib and Seaborn: Python libraries are used to produce static and interactive visualizations, which are crucial for successfully conveying insights.
  • Plotly: An adaptable instrument for making web-based and interactive visualizations that work with R, Python, and other programming languages.

Data Analysis and Manipulation Tools:

  • Pandas: A Python library for data analysis and manipulation, including methods for cleaning, transforming, and summarising data along with data structures like DataFrames.
  • SQL: The foundational language for organizing and querying relational databases. It allows students to work with authentic datasets and carry out data manipulation activities.

Machine Learning Libraries:

  • Scikit-learn: Extensive Python library for machine learning tools and algorithms, covering dimensionality reduction, clustering, regression, classification, and more.
  • TensorFlow and Keras: Popular deep learning frameworks allow students to work on complex machine learning projects by allowing them to construct and train neural networks.

Version Control Systems:

  • Git: Crucial for coding project collaboration and version control, enabling the tracking of modifications, branch management, and sharing of code repositories with instructors and peers.
  • GitHub: A Git repository hosting site that's popular for code sharing, project collaboration, and displaying work to employers.

Cloud Platforms:

  • Google Colab: A free cloud-based platform equipped with GPU and TPU access that allows Jupyter Notebooks to run, making it perfect for students to experiment with big datasets and intricate machine-learning models.
  • AWS Educate: Enables students to practice data science procedures in a cloud context by giving them access to cloud computing resources like S3 storage and Amazon EC2 instances.

Job Roles and Responsibilities for Data Science

  • Data Scientist: To gain insights and create prediction models, data scientists gather, preprocess, and analyze massive datasets. They collaborate with stakeholders to convert business requirements into data-driven solutions and disseminate the results via reports and visuals. It's critical to keep up with developments in data science. Enhance your skills and secure placement opportunities with our Data Science course with placement.
  • Data Analysts: Data analysts use SQL, Python, or R to clean, manipulate, and analyze data in order to find patterns and trends. They do statistical analysis to confirm findings and provide recommendations, and they provide dashboards and reports to aid in decision-making. Business objectives are supported when cross-functional teams collaborate.
  • Engineer for Machine Learning: Through feature engineering and tuning, machine learning engineers create and implement models to address business challenges and maximize performance. For integration, they work with software engineers, and for dependability, they keep an eye on model performance.
  • Data Engineer: To ensure data accessibility and quality, data engineers create scalable pipelines for data processing and storage. While keeping up with new developments in data engineering, they create ETL procedures and implement data warehousing solutions.
  • Intelligence Analyst for Business: As they create reports and visualizations to monitor company performance, business intelligence analysts define KPIs and metrics. Together, they enhance processes, carry out ad hoc analysis to gain insights, and assess strategy using insights from data.

Top Hiring Companies for Data Science

Data scientists are in high demand by businesses from various sectors that want to use data-driven insights to spur innovation and business expansion. The following well-known businesses are well-known for employing data scientists. Explore placement opportunities with our Data Science course with placement.

  • Google: Known for integrating machine learning and data analytics widely into its offerings, Google employs data scientists to work on everything from search engine optimization to advertising.
  • Facebook: Facebook hires data scientists to analyze user behavior, improve ad targeting, and improve the user experience on its platform because of its large user base, which generates tremendous volumes of data.
  • Amazon: One of the biggest cloud computing and e-commerce businesses in the world, Amazon uses data science for consumer analytics, supply chain optimization, and product suggestions.
  • Microsoft: Microsoft employs data scientists to create intelligent apps, enhance user experience, and spur innovation throughout its range of goods and services, with an emphasis on artificial intelligence and cloud computing.
  • Apple: Well-known for its cutting-edge goods and services, Apple hires data scientists to perform a range of tasks, including developing Siri, gaining consumer insights, and enhancing platform user engagement.
  • Netflix: A pioneer in the streaming media space, Netflix primarily uses data science to inform choices about content production, personalization, and suggestions.
  • Uber: This technologically advanced transportation corporation uses data science for ride optimization, pricing schemes, and driver-partner matching algorithms.
  • Airbnb: A well-known online travel and accommodation marketplace, Airbnb employs data scientists to optimize pricing, increase host-guest matching, and improve the user search experience.
  • LinkedIn: LinkedIn uses data scientists to analyze talent, make personalized recommendations, and optimize content. The company focuses on professional networking and recruitment.
  • IBM: A multinational technology and consulting firm, IBM offers various opportunities for data scientists by using data science to solve problems in industries including cybersecurity, healthcare, and finance.
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Top Skills Covered
  • Data Analysis
  • SQL Programming
  • Deep Learning
  • Natural Language Processing
  • Data Mining
  • Big Data Technologies
  • Data Science Algorithms
  • Data Visualization

Data Science Learning Key Features 100% Money Back Guarantee

  • 5 Weeks Training

    For Become a Expert
  • Certificate of Training

    From Industry Data Science Experts
  • Beginner Friendly

    No Prior Knowledge Required
  • Build 3+ Projects

    For Hands-on Practices
  • Lifetime Access

    To Self-placed Learning
  • Placement Assistance

    To Build Your Career

Top Placement Assistance

Our Data Science placement initiative simplifies the hiring process, guaranteeing that businesses acquire proficient professionals capable of deploying and enhancing Data Science solutions. Graduates can anticipate attractive salary packages coupled with avenues for significant pay raises supported by our placement support.

  • Designation
  • Annual Salary
    Hiring Companies
  • 4L
    Min
  • 8L
    Average
  • 20L
    Max
  • 3L
    Min
  • 6L
    Average
  • 16L
    Max
  • 3L
    Min
  • 6L
    Average
  • 14L
    Max
  • 4L
    Min
  • 7L
    Average
  • 15L
    Max

Training Options

One to One Training

₹23000₹ 18000

  • Exclusive one-on-one sessions for personalized skill development.
  • Latest version curriculum is provided by industrial professionals.
  • Get industry-recognized certification with placement assistance.
  • Lifetime Access to Study Materials, Videos & Top MNC Interview Question.
  • Work on multiple Industry-specific projects to enhance your portfolio.

Online Training

₹21000₹ 16000

  • preferred
  • Schedule your sessions at your preferred timings for maximum convenience.
  • Lifetime access to high-quality self-learning and live online class recordings.
  • Attend a Free Demo before signing up.

Next Demo Sessions

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Corporate Training

Customized to your team's needs

  • Self-Paced/Live Online/Classroom modes of training available.
  • Design your own course content based on your project requirements.
  • Comprehensive certification guidance for your career advancement.
  • Get 100% Job-oriented Training With Dedicated Placement Support.
  • Receive 24x7 assistance and support to maximize your learning experience.

Self Paced Training

  • 50+ Hours High-quality Video
  • 28+ Downloadable Resource
  • Lifetime Access and 24x7 Support
  • Access on Your Computer or Mobile
  • Get Certificate on Course Completion
  • 3+ Projects
12500 ₹4500

Data Science Course Curriculum

Trainers Profile

At Learnovita, our Data Science instructors are seasoned professionals holding certifications and boasting significant industry tenure within top multinational firms. They are dedicated to furnishing learners with advanced insights and the latest industry methodologies in Data Science, guaranteeing their preparedness to excel in today's competitive job market.

Syllabus of Data Science Online Course Download syllabus

  • Introduction to Data Science
  • Data Science Terminologies
  • Significance of Data Science
  • R Programming basics
  • Data engineering importance
  • Ecosystems of data engineering tools
  • Core concepts of data engineering
  • Python Introduction
  • Python Data Types & Operators
  • Flow Control statements & Functions
  • Structured vs Unstructured Data
  • Python Numpy package introduction
  • Array Data Structures in Numpy
  • Array operations and methods
  • Python Pandas package introduction
  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations
  • R Installation and Setup
  • R STUDIO – R Development
  • R language basics and data structures
  • R data structures , control statements
  • Statistics Importance
  • Statistics classification
  • Statistical terminology
  • Types of variables
  • Measures of central tendency
  • Measures of variability
  • Coefficient of variance
  • Skewness and Kurtosis
  • Machine Learning Fundamentals
  • Supervised Learning
  • Classification in Supervised Learning
  • Linear Regression and mathematical concepts
  • Classification Algorithms
  • Ensemble Learning techniques
  • Logistic vs Linear Regression
  • Poisson Regression
  • Bivariate Logistic Regression
  • Math related to logistic regression
  • Multivariate Logistic Regression
  • Building Logistic Models
  • False and true positive rate
  • Natural language processing
  • Text mining basics
  • Significance and use-cases of text mining
  • NPL working with text mining
  • Language Toolkit(NLTK)
  • Text Mining: pre-processing, text-classification and cleaning
  • Numpy Basics
  • Numpy Mathematical Functions
  • Probability Basics and Notation
  • Correlation and Regression
  • Conditional Probability, sum rule, and product rule
  • Joint Probabilities
  • Bayes Theorem
  • Tensorflow Basics
  • Tensorflow open-source libraries
  • Deep Learning Models and Tensor Processing Unit(TPU)
  • Graph Visualisation and keras
  • Keras neural-network
  • Define and Composing multi-complex output models
  • Batch normalization
  • Functional and Sequential composition
  • Implementing Keras with tensorboard
  • Customizing neural network training process
  • Implementing neural networks through TensorFlow API
  • Big Data and Hadoop Basics
  • Hadoop Architecture, HDFS
  • MapReduce Framework and Pig
  • Hive and HBase
  • Scala and Functional Programming
  • Introduction to Spark
  • Spark RDD Operations
  • Spark Transformations
  • Spark streaming introduction
  • Spark streaming Architecture
  • Spark Streaming Features
  • Structured streaming Architecture, Dstreams, and Spark Graphx
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Industry Projects

Project 1
Project for Chatbot Development

Chatbots use data science and AI with Python and Recurrent Neural Networks trained on intentions JSON dataset for personalized replies, streamlining customer questions and relieving pressure on support workers.

Project 2
Forecast for forest fires

One frequent and destructive calamity that requires a lot of effort to contain is forest fires. K-means clustering on meteorological data facilitates the identification of fire hotspots and the assessment of their intensity.

Project 3
Based on Road Lane Line Detection

For driving assistance systems (ADAS) and driverless cars, lane detection is essential. This project recognises lane markers on car camera images/videos using computer vision techniques including edge detection.

Mock Interviews

  • 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?

Data Science Training Objectives

Gaining knowledge in data science enables you to gain insightful information and make deft judgments from massive volumes of data, promoting success, efficiency, and innovation in a variety of sectors.
  • Fundamental Knowledge of Programming
  • Arithmetic and Statistics
  • Data Manipulation
  • Concepts of Machine Learning
  • Data Visualisation
  • Database Expertise
  • Critical Thinking and Problem-Solving
  • Integrated Development Environments (IDEs)
  • Tools for Data Visualisation
  • Version Control Systems
  • Big Data Tools
  • Data Wrangling and Cleaning Tools
  • Machine Learning Libraries
Across sectors, data science propels efficiency, innovation, and data-driven decision-making. Because there is a great need for qualified individuals, it powers personalized experiences, automates chores, and provides a variety of job options.
Data science has a bright and expansive future. The following are some crucial elements:
  • Increasing Demand
  • Emerging Technologies
  • Specialized fields
  • Ethical Considerations
  • Interdisciplinary Collaboration
  • Continuous Learning
  • Data Science Overview
  • Languages Used for Programming
  • Organization and Purification of Data
  • Data Analysis (EDA) for Exploration
  • An Examination of Statistics
  • Computer-Based Instruction
  • Data Visualisation
Because there is a great need for data scientists in India and the field pays well, there are fantastic chances to work in a variety of industries, including IT, banking, healthcare, e-commerce, and consulting.
  • Technological
  • Accountancy
  • Medical services
  • Retail
  • Telecommunications
  • Industrial Production
  • Lectures in video format
  • Engaging Instruction and Coding Tasks
  • Tests and Evaluations
  • Discussion boards on the internet
  • Talks with guests and webinars
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Data Science Exam & Certification

Several certifications are available in data science, including:
  • IBM Data Science Professional Certificate
  • Microsoft Certified: Data Analyst Associate
  • Google Data Analytics Professional Certificate
  • SAS Certified Data Scientist
  • EMC Data Science Associate (EMCDSA)

A data science certification increases employability and credibility by validating abilities. It demonstrates a dedication to study and opens doors to new employment options, particularly in healthcare analytics or cybersecurity. Certifications facilitate networking, frequently raising one's earning potential and keeping one abreast of industry developments for both international recognition and personal fulfillment.

Yes, obtaining a data science certification ensures employment, even though it improves credentials. Important factors include industry need, competitiveness, and experience. Although certifications emphasize experience and dedication on resumes, employers take into account several criteria.
  • Knowledge of Programming
  • Proficiency in Statistics and Mathematics
  • Experience with Data Analysis
  • Foundations of Machine Learning
  • Knowledge of Databases
Review the test schedule and prerequisites to prepare for a data science certification. Take practice tests, learn practical skills, and study the fundamentals. Become informed, enroll in our classes, and join study groups.

Recently Placed Students

Data Science Course FAQ's

Certainly, you are welcome to join the demo session. However, due to our commitment to maintaining high-quality standards, we limit the number of participants in live sessions. Therefore, participation in a live class without enrollment is not feasible. If you're unable to attend, you can review our pre-recorded session featuring the same trainer. This will provide you with a comprehensive understanding of our class structure, instructor quality, and level of interaction.
All of our instructors are employed professionals in the industry who work for prestigious companies and have a minimum of 9 to 12 years of significant IT field experience. A great learning experience is provided by all of these knowledgeable people at LearnoVita.
  • 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, HCL, 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.
LearnoVita Certification is awarded upon course completion and is recognized by all of the world's leading global corporations. The National Institute of Education (NIE) in Singapore and LearnoVita are the exclusive authorized Oracle, Microsoft, Pearson Vue, and PSI exam centers, as well as an authorized partner of AWS. Additionally, those who want to pass the National Authorized Certificate in a specialized IT domain can get assistance from LearnoVita's technical experts.
As part of the training program, LearnoVita provides you with the most recent, pertinent, and valuable real-world projects. Every program includes several projects that rigorously assess your knowledge, abilities, and real-world experience to ensure you are fully prepared for the workforce. Your abilities will be equivalent to six months of demanding industry experience once the tasks are completed.
At LearnoVita, participants can choose from instructor-led online training, self-paced training, classroom sessions, one-to-one training, fast-track programs, customized training, and online training options. Each mode is designed to provide flexibility and convenience to learners, allowing them to select the format that best suits their needs. With a range of training options available, participants can select the mode that aligns with their learning style, schedule, and career goals to excel in Data Science.
LearnoVita guarantees that you won't miss any topics or modules. You have three options to catch up: we'll reschedule classes to suit your schedule within the course duration, provide access to online class presentations and recordings, or allow you to attend the missed session in another live batch.
Please don't hesitate to reach out to us at contact@learnovita.com if you have any questions or need further clarification.
To enroll in the Data Science Course at LearnoVita, you can conveniently register through our website or visit any of our branches in India for direct assistance.
Yes, after you've enrolled, you will have lifetime access to the student portal's study materials, videos, and top MNC interview questions.
At LearnoVita, we prioritize individual attention for students, ensuring they can clarify doubts on complex topics and gain a richer understanding through interactions with instructors and peers. To facilitate this, we limit the size of each Data Science batch to 5 or 6 members.
The average annual salary for Data Science Professionals in India is 4 LPA to 7 LPA.
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