Python for Data Science – Hands On: 2-in-1

A practitioner's guide covering essential data science principles, tools, and techniques

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Python for Data Science – Hands On: 2-in-1


The markets of today are always open. With business happening round the clock, data is continuously being generated. If you know how, then you can extract useful information from this data This course teaches you how get your data to talk and tell you information that you can use to strengthen your business or organization.

This course begins by discussing what data science is and the role it can play in business development. You'll then learn the basics of Jupyter, which is the environment that you'll use for coding. Once you are comfortable with the basics, you'll delve into the anatomy of Numpy and learn ways to use it to your advantage. you'll learn how to create DataFrames and summarize and group data. As you progress, you'll learn how to plot graphs using Seaborn and Matplotlib. As the course advances, you'll be introduced to machine learning and explore ways to process data using techniques, such as k-means clustering and random forest. The later lectures show you how to finetune your application by optimizing the hyperparameters. Through linear and logistic regression, you'll learn how to train and fit the regressor in the training set. Before the course concludes, you'll also get an overview of unsupervised learning.

By the end of this course, you'll be ready to work on data science projects or developing your own data science applications.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

  • Ilyas Ustun is a data scientist. He is passionate about creating data-driven analytical solutions that are of outstanding merit. Visualization is his favorite. After all, a picture is worth a thousand words. He has over 5 years of data analytics experience in various fields like transportation, vehicle re-identification, smartphone sensors, motion detection, and digital agriculture. His Ph.D. dissertation focused on developing robust machine learning models in detecting vehicle motion from smartphone accelerometer data (without using GPS). He publishes from time to time on his website.

  • Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from Natural Language Processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meetups, conferences, and other events.

  • Luca Massaron is a data scientist and marketing research director specializing in multivariate statistical analysis, machine learning, and customer insight, with over a decade's experience solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about every aspect of data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, Luca believes that a lot can be achieved in data science just by doing the essentials.

What You Will Learn!

  • Learn data analysis, manipulation, and visualization using the Pandas library
  • Create statistical plots using Matplotlib and Seaborn to help you get insights into real size patterns hidden in data
  • Become proficient in working with real life data collected from different sources such as CSV files, websites, and databases
  • Get hands on with Numpy for numerical and scientific computation
  • Implement machine learning algorithms and delve into various machine learning techniques, and their advantages and disadvantages
  • Work with regression, classification, clustering, supervised and unsupervised machine learning, and much more!
  • Manipulate, fix, and explore data to solve data science problems

Who Should Attend!

  • This learning path is for data science entrants who wish to explore the world of data science by entering in it. This learning path is also for data analysts and data engineers to help them tackle real-world data science problems without wasting any time.



  • Data Science
  • Python






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