7 Ways you can learn Applied Data Science through Python

1. Seven Steps to put Applied Data Science into action through Python Data science is consistently becoming the need of the hour of every organization. Considering the amount of data that is transacted every day all over the world it becomes very important that this data is worked upon and manipulated in order to gain input and produce results. Ergo, becoming a data scientist will not only go a long way in making your career secure but also give you a job you could really innovate in. Data scientists today are supposedly doing the most innovative of jobs today ranging from machine learning to data mining and much more. Python is an amazing tool that you can use in order to gain advantage in the world of data science. Using Python for data manipulation and visualization can be an extremely simple and effective task. Here we will go through the steps that you must follow in order to gain access in the world of data science and manipulation through the Python. Step 1 Get warmed up and learn the basics. Before you dive head-first in the sea of data, you must understand the basics of both python and data science. Learn a bit about data manipulation and how data flow takes place in programming languages like R, SAS and python. Just an overview will do the job and there is no

2. need to go into the depths. Also learn the basics of Python and its environment and understand how it is different from other programming languages. Step 2 Set up Python for data analysis In order to set up an environment suitable for data analysis you can take two paths 1, Download Anaconda from continuum analytics It contains the core python language and also the important libraries that are needed for data analysis. It also contains an iPython notebook or the Jupyter notebook which is the environment you use to code. 2. Download Rodeo from Yhat This is another coding environment that can be used if you do not wish to use the browser based jupyter notebook. It entirely depends on your personal choice. Step 3 Learn the basics and fundamentals Once you are all set up, you can move to learning the basic coding stuff. This would include the fundamentals of python programming syntax, variables, list, Tuples , dictionaries and much more. It is very important that you learn the Regular Expressions of Python as they would be used a lot during data manipulation. For learning the basics of python with reference to data manipulation, a number of courses are available online on portals like Udemy, Aurelius, code academy and more.

3. Step 4 Deal with data manipulation packages and libraries The fun parts start here! There are various libraries available in python which allow data manipulation and handling. Here are some major Python libraries that you may use to work with data. It is important that you familiarize yourself with these in order to have a good time dealing with data. Download all of these libraries and get a basic understanding of them, after which you will be good to go! Numpy and Scipy Pandas MatlplotLib Scikit-Learn StatsModels Of these, Numpy, Scipy and Pandas are the most elementary and most important that you must get well versed in. Step 5 Data manipulation and practice You can initially begin with Pandas and Numpy in order to start practicing manipulation of data. To do this, just get a sample data set and start with transformations, formatting, cleaning, etc. The best way to learn data manipulation is always experimentation and practice. The more you practice the more insight you will get of the tool. Step 6 Data visualization and Analytics Visualizing and analyzing data is as important as manipulating it. Matlplotlib is probably the most used library for data visualization and is extremely easy to learn. Get trained hands on in Matplotlib using your sample datasets and create some visualizations of your own. Also, you may want to learn analytics techniques in order to analyze data that you are working on. Through the analytics procedures you can create statistical models, machine learning algorithms, data mining techniques and more. The most used library for data analytics are Scikit-learn and Stats-model. These contain the implementations of models and algorithms that you may want to use in your analysis. Start with familiar techniques of modeling such as Linear Regression, K-nearest Neighbor, Time series, etc. Step 7 Mastering and reporting

4. Communicating your results and analysis is as important in Data science as any other aspects. Preparing legible reports is a key soft skill in data science which must be effectively learned. In python you can use the same Jupyter networks that you use for editing in order to report your results and data. Using Jupyter you can represent your codes in various formats such as PDFs, HTML and markdown. Once you are through with the intial steps you may start with some advanced concepts of Data science with python by learning through the various resources available on the internet. You may also want to get your hands dirty with deep learning and get indulged in advanced concepts like neural networks and genetic algorithms. Mastering data science through python will require a whole lot of patience and hard work but it will definitely be worth the wait. Python is the most popular and most used language of the contemporary technological world and data science is probably the biggest technological trend that will continue to rise in the decades to come. Becoming a data scientist with Python as your tool of expertise may be one of the best things to go for in your professional career. Website: http://www.aurelius.in/ Address: A-125, Sector- 63, Noida 201307, Contact No. India +91.783.501.1153, USA: +1.650.681.9789, Australia: +61.390.164.274, Canada: +1.437.888.4902, UK: +44.208.123.2629