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Learning Python:

 

Before we explore how to learn Python for data science, we should briefly answer why you should learn Python in the first place.

In short, understanding Python is one of the valuable skills needed for a data science career.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

  • In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
  • In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
  • In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Data science experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

According to Indeed, the average salary for a Data Scientist is $127,918.

The good news? That number is only expected to increase. The experts at IBM predicted a 28% increase in demand for data scientists by the year 2020.

So, the future is bright for data science, and Python is just one piece of the proverbial pie. Fortunately, learning Python and other programming fundamentals is as attainable as ever. We’ll show you how in five simple steps.

But remember – just because the steps are simple doesn’t mean you won’t have to put in the work. If you apply yourself and dedicate meaningful time to learning Python, you have the potential to not only pick up a new skill, but potentially bring your career to a new level.

Why Enroll for the Python for Data Science course?

 

Python is a versatile programming language preferred by programmers and tech companies around the world, from startups to behemoths. Data scientists use it extensively for data analysis and insight generation, while many companies choose it for its ease of use, extensibility, readability, openness, and the completeness of its standard library.

This course is designed by BECLOUDREADY. All live training sessions will be delivered by Course Leaders from BECLOUDREADy, while recorded video lectures will be delivered by data science experts from DataCamp.

Who is this Course for?

  •  Participants with no prior programming experience who want to learn Python Programming as used in the field of data science

#1

fastest growing programming language

SOURCE: TechRepublic

28%

increase in data science jobs by 2020

SOURCE: Source IBM

65%

developers use & want to use Python

KEY TAKEAWAYS

After completing this course, participants will:

 

Understand the metrics that predict high performance in leading technology companies.

Get to know the best tools to implement projects using Cloud Computing and DevOps.

Understand the opportunities offered by DevOps and automation in new product development.

Be able to distinguish the advantages and disadvantages of different Cloud applications (Serverless, Cloud Native, etc.) and how use these technologies for maximum benefit in your organization.

Be familiar with the origins and future of Cloud Computing, and how this technology will impact new developments in tech and business.

Learn to implement security measures and improve performance in software design.

Understand the technicalities of containers and orchestration and how this type of programming can be applied in today’s work environments.

Understand the key tools behind effective software development and fundamental Cloud Computing and DevOps methodologies.

Step 1: Learn Python Fundamentals

Everyone starts somewhere. This first step is where you’ll learn Python programming basics. You’ll also want an introduction to data science.

One of the important tools you should start using early in your journey is Jupyter Notebook, which comes prepackaged with Python libraries to help you learn these two things.

Kickstart your learning by: Joining a community

By joining a community, you’ll put yourself around like-minded people and increase your opportunities for employment. According to the Society for Human Resource Management, employee referrals account for 30% of all hires.

Create a Kaggle account, join a local Meetup group, and participate in Dataquest’s members-only Slack discussions with current students and alums.

 

Step 2: Practice Mini Python Projects

We truly believe in hands-on learning. You may be surprised by how soon you’ll be ready to build small Python projects.

Try programming things like calculators for an online game, or a program that fetches the weather from Google in your city. Building mini projects like these will help you learn Python. programming projects like these are standard for all languages, and a great way to solidify your understanding of the basics.

You should start to build your experience with APIs and begin web scraping. Beyond helping you learn Python programming, web scraping will be useful for you in gathering data later.

 

Step 3: Learn Python Data Science Libraries

Unlike some other programming languages, in Python, there is generally a best way of doing something. The three best and most important Python libraries for data science are NumPy, Pandas, and Matplotlib.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs like you’d find in Excel or Google Sheets.

 

Step 4: Build a Data Science Portfolio as you Learn Python

For aspiring data scientists, a portfolio is a must.

These projects should include several different datasets and should leave readers with interesting insights that you’ve gleaned. Your portfolio doesn’t need a particular theme; find datasets that interest you, then come up with a way to put them together.

Displaying projects like these gives fellow data scientists something to collaborate on and shows future employers that you’ve truly taken the time to learn Python and other important programming skills.

One of the nice things about data science is that your portfolio doubles as a resume while highlighting the skills you’ve learned, like Python programming.

 

Step 5: Apply Advanced Data Science Techniques

Finally, aim to sharpen your skills. Your data science journey will be full of constant learning, but there are advanced courses you can complete to ensure you’ve covered all the bases.

You’ll want to be comfortable with regression, classification, and k-means clustering models. You can also step into machine learning – bootstrapping models and creating neural networks using scikit-learn.

At this point, programming projects can include creating models using live data feeds. Machine learning models of this kind adjust their predictions over time.

 

How Long Will It Take To Learn Python?

After reading these steps, the most common question we have people ask us is: “How long does all this take?”

There are a lot of estimates for the time it takes to learn Python. For data science specifically, estimates a range from 3 months to a year of consistent practice.

We’ve watched people move through our courses at lightning speed and others who have taken it much slower.

Really, it all depends on your desired timeline, free time that you can dedicate to learn Python programming and the pace at which you learn.

Dataquest’s courses are created for you to go at your own speed. Each path is full of missions, hands-on learning and opportunities to ask questions so that you get can an in-depth mastery of data science fundamentals.

Get started for free. Learn Python with our Data Scientist path and start mastering a new skill today.

CERTIFICATION

Upon successful completion of the course, participants will receive a verified digital certificate from BECLOUDREADY.

 

The estimated time needed to successfully complete the program is 8 – 10 hours per week.