Data Science Certification Course


Become a Data Science Certified Professional with the Elearners365 Course!

Key Highlights

40 hours of guided instruction

Certification upon completion

Hands-on projects

Flexible scheduling options

Job placement support

Ongoing support from mentors

Comprehensive curriculum

Live classes held daily

Data Science Course Overview

Data Science Course Overview: This course covers essential data manipulation, statistical analysis, machine learning, and visualization techniques using Python and R, equipping students with practical skills for real-world data analysis.

You will cover a wide range of topics, including AI fundamentals, machine learning basics, deep learning, NLP, computer vision, cloud computing, and Python for data analysis. Additionally, you’ll learn about data collection, business intelligence, data wrangling, exploratory data analysis, advanced Python skills, and MySQL. Further modules include data visualization, probability/statistics, various machine learning approaches, predictive analysis, advanced Python libraries, Azure fundamentals, model training, MLOps, job descriptions, and hands-on Azure experience.

This data science course is suitable for anyone interested in gaining skills and knowledge in the field of data science. It’s particularly beneficial for:

  • Aspiring data scientists
  • Professionals looking to transition into data science roles
  • Students seeking to enhance their data analysis skills
  • Individuals interested in AI, machine learning, and big data analytics
  • Business professionals aiming to leverage data for decision-making
  • Anyone curious about the intersection of technology and data-driven insights.

The prerequisites for taking up this data science course typically include a basic understanding of mathematics and statistics, familiarity with programming concepts (preferably in Python), and an eagerness to learn and engage with complex data analysis techniques. Some courses may also recommend prior exposure to data manipulation tools and techniques, but a strong willingness to learn and dedication to the subject matter are essential.

Choosing a data science course online from elearners365 offers several advantages. Firstly, it provides the flexibility to learn at your own pace, allowing you to balance your studies with other commitments. Additionally, elearners365 offers a comprehensive curriculum that covers essential topics in data science, ensuring you acquire the necessary skills and knowledge for success in the field. The courses are taught by experienced instructors who are experts in their respective domains, providing you with valuable insights and guidance throughout your learning journey. Furthermore, elearners365 incorporates practical, hands-on learning experiences, enabling you to apply theoretical concepts to real-world scenarios. Lastly, completing a data science course from elearners365 can enhance your career prospects, equipping you with in-demand skills sought after by employers in various industries.

Data Science Career Transition

Transitioning into a data science career involves acquiring skills in data analysis, machine learning, and programming, often through formal education, self-study, or bootcamps, to secure roles in data-driven industries.

Average salary increase of 59%

45 LPA Highest Salary

Learn programming languages

Engage with professional networks

Skills Covered

Tools Coverd

Cost of Enrolling in Data Science Training

Enrolling in data science training involves understanding the associated costs and potential benefits for informed decision-making. By evaluating expenses against expected outcomes, individuals can gauge the value of pursuing such education.

Group Batch

Rs 40,000

New Batch Starts Every Week

Upto 10 Student

1:1 Batch

Rs 90,000

New Batch Starts Every Week

Single Student

Data Science Course Syllabus

  • Basics of AI
  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Cloud Computing
  • Python {NumPy, Pandas, Matplotlib}
  • Additional Topic: Operating System
  • Data Collection
  • Business Intelligence
  • Data Wrangling
  • Exploratory Data Analysis
  • Python Lab {Pandas Adv, Tableau, Excel}
  • MySQL Labs
  • Additional Topic: Virtualization
  • Data Visualization
  • Probability and Statistics
  • Machine Learning Approaches
  • Predictive Analysis
  • Python Lab{ Sklearn, Keras, TensorFlow}
  • Additional Topic: Networking
  • Azure Fundamentals
  • Model Training
  • Pipelines
  • MLOps
  • Corporate Job Descriptions
  • Azure Hands-on Lab
  • Additional Topic: Cloud

Course Enquiry

Data Science Projects

Data Science Certification

In this Data Science Certification, you will learn fundamental concepts such as data manipulation, statistical analysis, machine learning, and data visualization. You will also gain practical experience with popular programming languages like Python and R, along with essential tools and techniques used in real-world data science projects.

To become a certified Data Scientist, you can pursue professional certification programs offered by renowned organizations or educational institutions. These programs typically require completion of specific coursework, hands-on projects, and passing a certification exam. Additionally, gaining practical experience through internships or working on data science projects can also enhance your qualifications and prepare you for certification.

The roles and responsibilities of a Data Scientist typically involve analyzing large datasets, identifying trends and patterns, developing predictive models, and communicating insights to stakeholders. Data Scientists also collaborate with cross-functional teams to solve complex business problems, optimize processes, and drive data-driven decision-making across organizations.

The validity period of a Data Science Certification varies depending on the certifying organization and the specific certification program. Typically, certifications remain valid for a few years before requiring renewal through continuing education or recertification exams. It’s essential to check the certification provider’s policies and requirements for maintaining your certification status.

Placement Target Companies

Data Science Certification Training FAQs

Data science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines techniques from statistics, computer science, and domain knowledge to solve complex problems and make data-driven decisions. Data science is important because it enables organizations to uncover valuable insights, improve decision-making processes, and drive innovation across various industries.

While prerequisites may vary depending on the course, having a basic understanding of mathematics, statistics, and programming is beneficial. Proficiency in programming languages like Python or R is often required, along with familiarity with concepts such as data manipulation, statistical analysis, and machine learning.

A data science course typically covers a range of topics, including data exploration and visualization, statistical analysis, machine learning, deep learning, big data technologies, and data ethics. Additionally, courses may include practical exercises, case studies, and real-world projects to provide hands-on experience in applying data science techniques to solve real-world problems.

Data science professionals are in high demand across various industries, including technology, healthcare, finance, retail, and marketing. Common job roles include data scientist, data analyst, machine learning engineer, business intelligence analyst, and data engineer. With the increasing reliance on data-driven decision-making, data science professionals can expect a wide range of career opportunities and competitive salaries.

To prepare for a data science course, consider brushing up on foundational concepts in mathematics, statistics, and programming. Familiarize yourself with popular data science tools and libraries such as Python, R, NumPy, Pandas, and scikit-learn. Additionally, stay updated on industry trends and developments by reading relevant blogs, books, and research papers. During the course, actively engage with the material, participate in discussions, and complete assignments and projects to reinforce your learning and gain practical experience.

Data science courses often provide a range of support resources to help students succeed, including access to instructors, teaching assistants, discussion forums, and online learning materials. Some courses may offer mentorship programs, career services, and networking opportunities to connect with industry professionals and peers. Additionally, students may have access to software tools, datasets, and computing resources needed for completing assignments and projects.

When evaluating data science courses, consider factors such as the course curriculum, instructor qualifications, student reviews and testimonials, accreditation status, and industry recognition. Look for courses that offer comprehensive coverage of relevant topics, hands-on learning opportunities, and practical experience. Additionally, research the reputation of the course provider or institution and assess whether the course aligns with your learning goals and career aspirations.