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Certificate in Data Science and Analysis

A comprehensive 12-month certification course designed to equip learners with core skills in data analysis, statistics, data visualization, and machine learning, preparing them for careers in data science and analytics.

Course Image
Course Details
Code NCP-37858 Type Certificate Course
Level Professional Level Sector Artificial Intelligence
Mode Offline Language English, Regional Language
Duration 12 Months Schedule Classes/week ( each)
Certification by National Council for Social Development and Education (NCSDE)

Course Overview

This 12-month Data Science and Analysis certification course provides a strong foundation in data analysis, statistics, and visualization, with a focus on real-world applications. Students will learn essential tools such as Python, SQL, and data visualization platforms like Tableau and Power BI. The course will cover statistical analysis, exploratory data analysis (EDA), machine learning algorithms, and big data processing, while also focusing on how to effectively communicate data insights to stakeholders. Throughout the course, learners will engage in hands-on projects, industry case studies, and capstone projects that simulate real-world problems. By the end of the course, students will be fully prepared to enter the data science and analytics industry with the skills required to solve complex business problems.

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Description

By completing this course, learners will gain essential competencies in data science and analytics, preparing them for diverse roles in data-driven industries. The course covers a wide range of tools and techniques, blending programming, statistical analysis, and visualization. Whether pursuing a career as a data analyst, business intelligence professional, or data scientist, learners will develop both technical and analytical skills to thrive in the field.

Key Competencies:

  1. Data Science Programming

    • Tools & Languages: Python, SQL, R

    • Master data manipulation and analysis with libraries like Pandas, Numpy, and Matplotlib.

    • Understand data structures, algorithms, and how to process large datasets.

  2. Statistical Analysis & Hypothesis Testing

    • Apply statistical techniques for data analysis.

    • Conduct hypothesis testing and significance tests for making data-driven decisions.

  3. Data Visualization

    • Visualize data using tools like Tableau, Power BI, and Matplotlib.

    • Develop dashboards and interactive reports to communicate insights effectively.

  4. Exploratory Data Analysis (EDA)

    • Perform data cleaning, feature engineering, and data exploration techniques.

    • Identify patterns, trends, and outliers in datasets.

  5. Machine Learning Basics

    • Learn key machine learning algorithms: regression, classification, clustering.

    • Build and evaluate predictive models using Scikit-learn and other libraries.

  6. Big Data Analysis

    • Introduction to big data tools like Hadoop, Spark, and NoSQL databases.

    • Process and analyze large datasets efficiently for scalable solutions.

  7. Business Acumen & Data-Driven Decision Making

    • Understand how to use data to make business decisions.

    • Learn how to present data insights to stakeholders in an actionable format.

Description

This course is designed to provide an immersive learning experience in data science and analysis. Through a combination of theory, hands-on practice, and industry exposure, students will gain the necessary skills to excel in the data science field.

  1. Comprehensive Curriculum

    • The course covers fundamental and advanced topics in data science and analysis.

    • It includes programming, data cleaning, exploratory data analysis, statistical analysis, and machine learning.

  2. Hands-On Projects

    • Students will work on real-world datasets, providing practical experience.

    • Capstone project allows students to apply learned skills to a complex, industry-relevant problem.

  3. Industry-Relevant Tools & Software

    • Master industry-standard tools like Python, R, SQL, Tableau, and Power BI.

    • Learn big data platforms like Hadoop and Spark for scalable solutions.

  4. Expert-Led Learning

    • Gain insights from instructors who are seasoned professionals in the data science field.

    • Access guest lectures, webinars, and workshops by top industry experts.

  5. Interactive Learning Platform

    • Access to an online platform for self-paced learning, assignments, and quizzes.

    • Discussion forums and peer collaboration for a dynamic learning environment.

  6. Job Readiness & Career Support

    • The course includes job placement assistance, resume building, and interview preparation.

    • Networking opportunities with industry professionals and hiring companies.

Description

The Data Science and Analysis certification course includes a broad range of resources and learning materials designed to give students the best possible learning experience. From interactive sessions to hands-on projects, students will receive support throughout the course.

  1. Study Materials

    • Access to video lectures, e-books, and detailed course notes.

    • Step-by-step guides for key concepts in programming, data analysis, and machine learning.

  2. Live Sessions & Webinars

    • Regular live Q&A sessions with instructors to clarify doubts.

    • Webinars by industry experts on data science trends, tools, and career paths.

  3. Practical Coding & Assignments

    • Hands-on coding exercises to practice Python, SQL, R, and data analysis skills.

    • Weekly assignments to reinforce key concepts learned in the lessons.

  4. Capstone Project

    • A final project where learners will apply their knowledge to solve a real-world business problem.

    • Guided support to help learners implement and present their project.

  5. Data Tools and Software Access

    • Full access to popular data analysis and machine learning tools like Tableau, Power BI, and Jupyter Notebooks.

    • Use cloud platforms like AWS and Google Cloud for deploying projects and analyzing data.

  6. Certification

    • A recognized certification upon successful completion of the course.

    • Certificate details can be shared on LinkedIn, resumes, and job applications.

  7. Career Support

    • Resume-building workshops, interview coaching, and career counseling.

    • Access to job portals and internship opportunities in the data science field.

Description

Data Science and Analysis is one of the highest-paying fields in the tech industry, and the demand for professionals is growing rapidly across sectors such as finance, healthcare, retail, and tech. Upon completing this course, learners can pursue a variety of well-compensated roles, with salaries varying based on the job title, experience, and location.

Income Opportunities:

  1. Data Analyst

    • Salary Range: ₹4,50,000 - ₹8,00,000/year

    • Data analysts interpret data and provide actionable insights to businesses. The role requires proficiency in data visualization, statistical analysis, and reporting.

  2. Data Scientist

    • Salary Range: ₹8,00,000 - ₹15,00,000/year

    • Data scientists build models to make predictions based on historical data. They work with machine learning algorithms and have strong programming and statistical knowledge.

  3. Business Intelligence Analyst

    • Salary Range: ₹6,00,000 - ₹10,00,000/year

    • Business intelligence analysts use data to inform strategic decisions, developing reports and dashboards to visualize data insights for stakeholders.

  4. Machine Learning Engineer

    • Salary Range: ₹10,00,000 - ₹18,00,000/year

    • These engineers design, develop, and deploy machine learning models to automate tasks or improve business processes. A strong programming background is essential.

  5. Data Engineer

    • Salary Range: ₹7,50,000 - ₹12,00,000/year

    • Data engineers focus on creating the infrastructure for data generation, collection, and storage, ensuring data pipelines are efficient and scalable.

  6. Freelance Data Consultant

    • Hourly Rate: ₹1,200 - ₹5,000/hour (depending on experience)

    • Freelance consultants work with businesses to solve data-related problems and help them utilize data for better decision-making.

Data science professionals are in high demand across industries such as finance, retail, tech, and healthcare, offering excellent salary packages and career growth opportunities. As businesses continue to become more data-driven, the income potential in this field remains strong, with numerous paths for advancement in roles and income.