Python for Data Analytics and Machine Learning Course Overview

Python for Data Analytics and Machine Learning Course Overview

The Python for Data Analytics and Machine Learning course is designed to equip learners with the essential skills required to analyze data and build machine learning models using Python. This comprehensive program starts with a Course Introduction to set expectations and outline the learning journey. It progresses through a series of modules beginning with a Jupyter Overview, which is an essential tool for data science.

Learners will get a solid foundation in Python before diving into specialized libraries like Numpy and Pandas for data analysis. Visualization techniques are covered extensively with libraries such as Matplotlib, Seaborn, Plotly, and more. As the course advances, it delves into machine learning topics, starting with an Introduction to Machine Learning and then exploring various algorithms and methods like Linear Regression, Logistic Regression, K Nearest Neighbors, and others.

The course includes practical Data Capstone Projects for hands-on experience, and it concludes with cutting-edge topics such as Neural Nets and Deep Learning and Big Data and Spark with Python. This course is ideal for those looking to enhance their data analytics and machine learning capabilities, providing them with the knowledge and tools to tackle real-world data challenges.

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  • Live Online Training (Duration : 40 Hours)
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Classroom Training price is on request

  • Live Online Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

  • 6 Months Access to Videos
  • Access via Laptop, Tab, Mobile, and Smart TV
  • Certificate of Completion



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Course Prerequisites

To ensure a productive and successful learning experience in the Python for Data Analytics and Machine Learning course, the following minimum prerequisites are recommended:

  • Basic understanding of programming concepts: While prior experience with Python is helpful, a fundamental understanding of any programming language is sufficient to get started.
  • Familiarity with fundamental data structures: A grasp of basic data structures like lists, sets, tuples, and dictionaries will facilitate learning Python’s data-handling capabilities.
  • Comfort with basic mathematics: Knowledge of high school level mathematics, especially algebra and a bit of statistics, is important, as these concepts are often used in data analysis and machine learning.
  • Problem-solving skills: Ability to think logically and problem-solve will help in understanding algorithms and machine learning models.
  • Willingness to learn: Most importantly, a strong desire to learn and explore the fields of data analytics and machine learning is essential for success in this course.

No prior experience in data analytics or machine learning is required. The course is designed to take you from the basics of Python programming to advanced data analysis and machine learning techniques.

Target Audience for Python for Data Analytics and Machine Learning

Koenig Solutions' Python for Data Analytics and Machine Learning course caters to aspiring data professionals seeking practical Python skills.

  • Data Analysts
  • Data Scientists
  • Machine Learning Engineers
  • Software Developers interested in data science
  • Business Analysts looking to leverage data analytics
  • Graduates pursuing a career in data-driven fields
  • IT Professionals aiming to expand into data roles
  • Research Scientists wanting to apply machine learning to their research
  • Statisticians seeking to enhance their analytical toolset
  • Product Managers wanting to make data-driven decisions
  • Entrepreneurs who wish to harness data insights for business intelligence
  • Marketing Professionals looking to interpret customer data and trends

Learning Objectives - What you will Learn in this Python for Data Analytics and Machine Learning?

Introduction to Learning Outcomes and Concepts

Gain practical skills in Python for data analytics and machine learning, mastering tools and techniques from basic scripting to advanced algorithms and data visualization methods.

Learning Objectives and Outcomes

  • Understand the Python programming language and its application in data analysis.
  • Utilize Jupyter Notebooks for interactive coding sessions and data visualization.
  • Perform data manipulation and analysis using Pandas and Numpy libraries.
  • Create a variety of data visualizations using Matplotlib, Seaborn, Plotly, Cufflinks, and built-in Pandas functions.
  • Apply geographical plotting for location-based data insights.
  • Complete capstone projects that consolidate data analytics skills with real-world datasets.
  • Grasp fundamental concepts of machine learning and its implementation in Python.
  • Build predictive models using algorithms such as Linear Regression, Logistic Regression, and K Nearest Neighbors.
  • Implement advanced machine learning techniques including Decision Trees, Random Forests, Support Vector Machines, and K Means Clustering.
  • Explore dimensionality reduction with Principal Component Analysis and develop recommender systems and natural language processing applications.


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“It is an interesting story and dates back half a century. My father started a manufacturing business in India in the 1960's for import substitute electromechanical components such as microswitches. German and Japanese goods were held in high esteem so he named his company Essen Deinki (Essen is a well known industrial town in Germany and Deinki is Japanese for electric company). His products were very good quality and the fact that they sounded German and Japanese also helped. He did quite well. In 1970s he branched out into electronic products and again looked for a German name. This time he chose Koenig, and Koenig Electronics was born. In 1990s after graduating from college I was looking for a name for my company and Koenig Solutions sounded just right. Initially we had marketed under the brand of Digital Equipment Corporation but DEC went out of business and we switched to the Koenig name. Koenig is difficult to pronounce and marketeers said it is not a good choice for a B2C brand. But it has proven lucky for us.” – Says Rohit Aggarwal (Founder and CEO - Koenig Solutions)
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