Master Python for Data Science in 30 Days

“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard, Gartner.

Ever wondered why you see advertisements on Youtube or Facebook or Google sites related to your current searches?

Don’t you think these ads make you buy luxury products more instead of necessary ones?

Just like carbon footprints, everyone has digital footprints. These digital footprints are used to recognise individuals and their habits. For example, Google collects the data on every person’s location, fitness, browsing history etc. Based on this data, they are able to decide what type of advertisements would one be interested and provide customized ads and recommendations. Similarly, many companies collect data on customers, to see how they can increase their sales or decrease their losses. All this is done using Data Science. Data Science is a broad term that uses scientific methods, algorithms and systems to extract useful information and transform it into valuable insights.

With the increase in the collection of data, analysing data is becoming very important. But to do that one should be well-aware of available tools and how to use them. One of the most popular programming languages when it comes to data analysis, data visualization, machine learning and Data Science is PYTHON.

Why use Python?

The main reason is that it is easy to understand and versatile. Also, it is developed under an OSI-approved open source license which simply means that it is freely usable and distributable. In addition to this, the software has a supportive community and hundreds of packages and libraries to help coders.

Due to the extensive amount of material available online, most of the time learners tend to do a lot of online courses and workshops but still aren’t able to develop confidence while coding in Python. The reason behind this is the lack of practice and imagination. One should always remember that imagination is the key to coding. If you can imagine what you want, you can always code it. So, never focus on memorising the syntax. It can found on Google. Focus on expanding your imagination. Try new things. The first step should be to ideate, then imagine and finally implement it. Don’t just copy-paste the solutions. Think and understand coding.

I’ve struggled a lot to learn Python myself. There are so many courses and mixed reviews available that one can’t decide which one fits the best.

Then, how to learn Python?

I’ve undertaken several Python courses available on Coursera, Udemy, Datacamp etc. From that experience, I can say that Complete Python Developer: Zero to Mastery by Andrei Neagoie on Udemy is the best among them. The instructor is knowledgeable and provides a great platform to connect to like-minded people (through Discord) and even team up to contribute to open-source projects on Github. This is a 31-hour comprehensive course which covers the basic and advanced Python along with various career options for a Python Developer. It also has several interesting, small, guided python projects to test your knowledge.

The most important thing to understand is that merely doing the course won’t make you an expert. You have to put some extra effort to master the skills you’ve learnt.

If you’ve never done any programming, I’d recommend you to take the above course and then follow the 30-day plan described below. For those who have done any programming before, follow the path I’ve mentioned below.

The Smart Path to excel Python in 30 days

Always remember Practice makes the master. In programming, practice makes you understand syntax and get you accustomed to it. For this purpose, I’ve made some repositories on Github for everyone to fork (copy the repository from Github) and practice. These exercises will help you to imagine and code. Every repository mentioned below has a “README” document which will help you to understand what has to be done each day. I’ve even laid out a 30-day plan for everyone to learn and understand Python. This plan will not only help you to master Python but also help you to improve your CV which in turn would help you get internships in near future.

So, let’s dive into the roadmap:

(Please make Github and Kaggle accounts before proceeding forward)

Day 1: Take Kaggle Micro course on Python (https://www.kaggle.com/learn/python). This will help you to get started and give you a brief introduction on Python, its data types and basic commands.

Day 2 to Day 4: Follow Python 3 Day Challenge (https://github.com/Anjali001/Python3DayChallenge). This will help you to further understand the basics of python and get you started with python programming.

Day 5 to Day 10: Follow Python 6 Day Challenge (https://github.com/Anjali001/Python6DayChallenge). In these 6 days, you will learn in-depth about how to use various data types and why are they important.

Now we’ve learnt the basics of Python. These basics will help you to understand python and learn various packages available in Python. Next, we move onto learning two important packages for data analysis and manipulation: Pandas and Numpy. In data science, these packages are essential libraries that everyone should know.

Day 11 to Day 25: Follow Python 15 Day Challenge (https://github.com/Anjali001/Python15DayChallenge). This challenge prepares you well for the datasets you’ll see in future. Importing packages, commonly used functionalities, loading data, working with data frames, handling missing values, data cleaning etc. are all included in this challenge. Once, you do this challenge, you will excel these libraries and get ready to do Data Science problems.

Congratulations!!

These small exercises of datasets to enhance your understanding and play an important role to distinguish your knowledge from others.

Now we have a strong foundation of Python to move to Machine learning part.

Day 26 to Day 30: Do Micro courses of Machine Learning on Python (https://www.kaggle.com/learn/intro-to-machine-learning). This will help you in knowing what it is and how to do it. Personally, I’d also recommend you to do the loan prediction free course from Analytics Vidhya (https://courses.analyticsvidhya.com/courses/loan-prediction-practice-problem-using-python). I have done this myself and so I can assure you that you’ll get a lot of help from this on how to approach a Machine Learning problem. This course will guide you through making hypothesis, data cleaning, data engineering, data visualization, univariate and bivariate analysis and finally making an ML algorithm.

Tip: Make notes when solving an ML problem, this will help you to get used to the process of solving it and also remembering the proper usage of various algorithms.

Way Forward: To further expand your knowledge, I’d recommend you to do practice problems available at Analytics Vidhya Platform. I’m sure these will help you to practice the machine learning part.

Kudos!! We have learnt everything.

Now, get ready and apply to internships on Internshala and don’t forget to put your Github website, Kaggle certificates and Analytics Vidhya practice problems on your CV. This will surely help you get an upper hand in the selection process.

Hope you loved it. For more such articles on data science, follow me on medium. If you have any questions, please leave it in comments.