Over the years, data science has evolved as one of the promising and sought-after career paths. With increasing development and the urge to learn Python, the data science experts predict that there will be increased demand of skilled data scientists in the near future.
While you are on your journey of discovering the answer for why you should learn Python in the first place to become a data scientist, you will be glad to know that there is a tremendous hike and boom in the number of growing job opportunities across the world. Data science is widespread and the need for data professionals who will be skilled in organizing and analyzing massive amounts of data will be at the top-notch in the technological world.
What is Data Science?
To become a master in data science, many people say, you can’t become a qualified data scientist, unless you master statistics, programming, databases, deep learning, natural language processing, and the list goes on. That’s so not true! Do you know what data science exactly is?
Well, let’s break down into pointers:
- There is a scenario and you have a question in your mind related to it.
- Begin your research by asking a question? How? Why?
- Gather all the relevant data that might help you solve the query.
- Clean the cluttered data that you have with you.
- Explore, analyze, and try to visualize the data.
- Finally, build a machine learning model to evaluate the results for the scenario
- Discuss with your team.
So, if you see, the process of data science is not only about math, it is about knowing a programming language, and the understanding of using the data in that language too. To become a data scientist, you will need the above skills such as NLP, or deep learning or any other, to solve a problem one day, but to begin your career in data science doesn’t necessarily mean to excel in all spectrum at the beginning itself!
You can begin your career path in data science either tomorrow, today, or now!
So, welcome to the exciting world of data science!
5 Best Ways to Learn Data Science With Python:
Following these simple steps will help you understand how you can begin your career in Data Science, and one day be at the highest position that you have dreamt of!
Step 1: Get Comfortable With Python Fundamentals
Well, everybody has to start somewhere in learning so that one has better job opportunities in future. The very first thing that you should do to begin learning in data science is to learn the python programming language. Python and R both are popular choices, however, focus on one language and its related ecosystem of data science packages. If for instance you have chosen Python, begin with configuring your programming environment Jupyter Notebook by installing Anaconda distribution as it eases the process of package installation and management on Windows, OSX, and Linux. The Anaconda distribution is one of the in demand Python distributions used for data science and is pre-packaged with all the Python libraries. While learning Python, focus on all the basics of the language such as comparisons, loops, data structures, imports, functions, etc.
Step 2: Python Data Science Library
Though Python is an interpreted language, it is considered slow for numerically heavy algorithms in comparison to C or Fortran. So, you still might wonder why is Python the most popular programming language for data science? The answer is, you can simply offload to the lower layer, the number-crunching tasks, just in the form of C or Fortran extension. To achieve this, Numpy and Pandas are excellent data science libraries in Python.
Begin with learning Numpy, as it is the most basic data structure of most Machine Learning algorithms which provides objects for multi-dimensional arrays. Whereas, Pandas provides in-memory 2D table objects called the DataFrame. While working with Python, it is essential to learn how to use the pandas library. The data structure “DataFrame”, is suitable for representing tabular data with columns of different types, such as in the case of Excel sheets or a SQL table. Data scientists spend most of their time in using the tools that the DataFrame includes such as for reading, writing, filtering, and cleaning data, handling any missing data, merging datasets, etc. With pandas, you can perform data analysis, data manipulation, and data visualization.
Step 3: Learn Basic Statistics With Python
People having aspirations to become Data Scientists, directly dive into Machine Learning without even understanding the Python Statistics Fundamentals (i.e How to Describe Your Data). It is important to know that statistics is the foundation of Data Science. In Python, StatsModels is a popular Python library for users to explore data, perform statistical tests, and estimate statistical models. Understanding all the concepts of statistics such as Mean, Median, Mode, Standard Deviation, and many other statistical inference learnings through practical implementation and not just theoretical implementation, is important to become a good Data Scientist.
Step 4: Learn Machine Learning With Scikit-Learn
Scikit-Learn is one of the popular Machine Learning Libraries. To build machine learning models to extract data or predict the output is the amazing part of Data Science, and for that purpose, you have to learn how to use the Scikit-Learn library. It offers a high-level interface for many tasks, and comes with tuning parameters for each model, and it helps you to understand the models as well as how to use them.
Step 5: Build your Data Science Portfolio & Keep Practicing
The best advice is to keep learning and implementing the concepts of Data Science, Python, and Machine Learning to sharpen your skills. Practice the things that you have learned, build data science projects with different datasets, get comfortable with regression, classification, and k-means clustering models, participate in Kaggle competitions, learn from online courses, etc to master your skill sets. If you have created your own data science projects, sharing them on GitHub can highlight your skills in the community of fellow data scientists as well. By sharing so, your portfolio will play a role in highlighting the skills you’ve learned, in your resume to reap better job roles.
So, to sum it up, data science is a vast field and an ever-growing industry too. You can pursue the online python course, data science course, and machine course to enhance your learning and become qualified for top job roles in the world of Data Science such as data scientists, data analysts, data architects or data engineers.
Once you are geared up with all your learnings and become a data science professional, you will be rewarded for your highly technical skill sets with lucrative paying jobs. Lastly, always remember the key is to keep learning.
So far you have just begun with your data science journey. There is so much more to learn in the field of data science, and you don’t have to excel in everything, you just need to get started!