Course Description

Welcome to Introduction to Data Science! This course is designed for beginners who are eager to dive into the world of data science and analytics. Data science is a multidisciplinary field that combines programming, statistics, and domain knowledge to extract insights and knowledge from data. In this course, you will learn the fundamental concepts, tools, and techniques used in data science, and how to apply them to real-world problems.

By the end of this course, students will
  • Gain a solid understanding of the data science lifecycle, including data collection, cleaning, exploration, analysis, and visualization.
  • Learn how to use programming languages such as Python and libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
  • Explore statistical concepts and techniques essential for data analysis, including probability, hypothesis testing, and regression analysis.
  • Understand different types of data and how to preprocess and transform them for analysis.
  • Discover the importance of data visualization and storytelling for communicating insights effectively.
  • Gain hands-on experience through practical exercises, projects, and case studies using real-world datasets.
  • Develop critical thinking and problem-solving skills necessary for tackling complex data science problems.

Course Content

  1. Definition and scope of data science
  2. Data science lifecycle and workflow

  1. Introduction to Python programming language
  2. Data types, variables, and operators
  3. Control flow and functions

  1. Introduction to Pandas library
  2. Loading and cleaning data
  3. Data manipulation and exploration

  1. Introduction to data visualization
  2. Basic plotting techniques with Matplotlib
  3. Advanced visualization with Seaborn

  1. Descriptive statistics
  2. Probability distributions
  3. Hypothesis testing and p-values

  1. Overview of machine learning concepts
  2. Supervised learning, unsupervised learning, and reinforcement learning
  3. Model evaluation and validation techniques

  1. Real-world applications of data science in various domains
  2. Case studies and projects demonstrating data science techniques

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Course Features

Online Training

Offline Training

Flexible Training

Industrial Training

Corporate Training

Digitilized classroom

Frequently Asked Questions

Is Data Science easy to learn?

Yes, a student can easily learn Data Science with the help of an expert trainer.

Do I have to spend much time learning the course?

No, the students can enroll for the course and complete it within 3 Weeks.

Is theoretical knowledge enough for completing the course?

No, the students have to do internships and training along with the course. At AGEIS Technova, the students are also given practical training.

How can I get a job in a Data Science Profile?

You must acquire enough knowledge and skills in Data Science before applying for the course. That’s why the candidates can enroll in the course from the best institute.

Is Data Science course in demand?

Of course yes, with the advancement of technology, courses like Data Science are in great demand.

Start Learning Today!

Contact AGEIS Technova Team & Get all your queries resolved