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Diploma in Using Python for Data Science

Learn how to use your basic Python knowledge and turn it into a career in data science with this free online course.

Publisher: Juan Galvan
Do you have a sound knowledge of Python and would like to start a promising career in data science? With this free online data science certification, you will learn how to apply Python concepts and compound data structures to data science through many different techniques. The content includes understanding numerical Python data analysis, statistics and probability, along with much more to set you on a path of data science success.
Diploma in Using Python for Data Science
  • Durée

    6-10 Heures
  • Students

  • Accreditation






View course modules


Do you have some experience using Python? If yes, then this free online will open your eyes to new business possibilities. As recruiters struggle to get well-qualified candidates for data management positions, this course will undoubtedly unlock the door for you and allow you the opportunity to gain employment at the company of your dreams. Large firms and organizations need data scientists for top-level management and as a data scientist or data engineer, you will be called upon to source, manage and analyze tremendous amounts of unstructured data. The results of your work will be examined and communicated for the strategic decision-making of the company. Why is Python a cornerstone of this kind of decision-making? Recently, Python emerged as the best programming language for data science. Apart from being very easy to learn and implement, Python is also a high-level programming language with vast applications across many industries.

Python experience alone is unfortunately not enough to make a great, sought-after data scientist. As you work through the data science online course, knowledge in statistics and probability will stand you in great stead in order to gain the most out of the content. Statistics as a subject is divided into two branches, descriptive statistics and inferential statistics. You will be provided with an overview of descriptive statistics used in data science, which is more suitable for data representation. Then, we will cover terms such as distributions and measures of asymmetry related to inferential statistics. Following statistics and probability, you will learn about data visualization, a discipline that tries to understand data by placing it in a visual context. Data visualization allows patterns, trends, and correlations to be exposed for the data scientist’s work.

Finally, you will learn that Python provides excellent libraries for graphing information. These are Matplotlib, Seaborn, and Pandas visualization. Pandas visualization is regarded as the best library of the three libraries and you will work through these libraries and the essential points to remember for any implementation in Python. This data science course also guides you in building your career in data science. Writing a good cover letter and an outstanding resume is vital, along with the necessity to reach out to recruiters to network and build rapport so you can apply for a data science position successfully. Obtain the best freelance websites to start data science projects and secure adequate experience to obtain better-paying jobs. Learn about personal branding, social networking and the importance of having a website. You should strongly consider enrolling in this in-depth, practical student if you are a student or professional who understands Python and want to use this knowledge to branch out into the field of data science.

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