Data Analyst is one of the hottest professions of the time. To Learn Python, it is easy for any programming student and little effort-based for students with the non-programming background. Here in this blog, you are going to learn how to learn Python online is helpful for data analysis. Python is in trend these days and its community support is tremendous. Once you are a Python expert, you will be able to solve any data analysis problem with ease. All you need is get complete knowledge of Python and study Python with complete dedication.
Learning Python for Data Analysis –
Python is gaining interest in the IT sector and the top IT students opt to learn Python as their choice of language for learning data analysis. The candidates want to jump into the career of a Data Analyst must have knowledge about some language and if we compare Python with other languages, Python is much more interesting and easy to learn as compared to other programming languages. Thus, it has become a common language for data analysis. Python is easy to learn and use whether you are new to the language or you are an experienced professional in information technology. Python helps you serve the company as a great data analyst.
Step 0: Warming up
Before starting your journey, the first question to answer is:
Why use Python?
How would Python be useful?
Refer to Top TED Talks on Data Analytics to get an idea of how useful Python could be.
Step 1: Setting up your machine
There are 2 approaches to install Python:
- You can download Python directly from its project site and install individual components and libraries you want
- Alternately, you can download and install a package, which comes with pre-installed libraries. I would recommend downloading Anaconda.
Second method provides hassle-free installation and hence We’ll recommend that to beginners. The imitation of this approach is you have to wait for the entire package to be upgraded, even if you are interested in the latest version of a single library. It should not matter until and unless, until and unless, you are doing cutting edge statistical research.
If you have any trouble in installing python, refer to Getting started with installing Jupyter Notebook
After the installation process is done, you are required to choose the environment for your work field. The options for choosing the environment include a terminal or shell-based environment, IDLE and iPython notebook. IDLE is set as the default environment and can be used as the most common environment for the users. The environment you choose depends on the requirements you need for coding.
Step 2: Learn the Basics of Python
You should start by understanding the basics of the language, libraries, and data structure.
All the languages have their own data structures and libraries. Similar is in the case of Python. Python has different data structures that help in making the code.
Some of the data structures are:
Lists – Lists are flexible data structures of Python that has the features to change each element of the list. A list can be described by writing a list of elements or values separated by a comma within the square brackets.
Strings – Strings in Python are defined by commas. It may be a single, double or triple inverted comma. Triple comma quotes are used for docstrings for multiple lines. Once the value is added into the strings, it cannot be changed.
Tuples – Tuples are described by the elements or values separated by commas. The values in the tuple cannot be changed or modified. They work much quicker than lists.
Dictionary – Dictionary is an unordered set of keys. The keys need to be unique to make the set as a dictionary. A dictionary contains a set of unique values. An empty dictionary is made up of a pair of braces. You can learn more about the Python Dictionary here.
Step 3: Learn Scientific libraries in Python
Libraries are very helpful for the ones who wish to learn Python. Before using any library, you need to import that library into your environment. Let us learn some important libraries used in Python for scientific calculations and data analysis.
NumPy – Numerical Python is the most dominant library in Python. Its most commanding characteristic is its n-dimensional array with the help of which n-dimensional quantities can be solved. Basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration are also present in NumPy just like the features of low-level languages such as Fortran, C, and C++.
SciPy – Scientific Python is an important and useful library for you if you want to use various high-level engineering modules such as discrete Fourier transform, linear algebra, optimization, and Sparse Matrices.
Matplotlib – Matplotlib is the library used for the purpose of plotting a large number of graphs whether they are from histograms or from heat plots. The important feature that iPython notebook include for plotting is Pylab feature to use inline plotting. If you don’t use an inline option in iPython environment, The Pylab will convert iPython environment to the Matlab environment. In order to add math in your plot, you can use Latex commands.
Pandas – Pandas are the most commonly used libraries in Python for data munging and preparing data operations. Pandas are used for structured data procedures and planning. The usage of Python is increased after the addition of Pandas into it. Pandas help in enhancing Python among data scientists for further research and analysis.
Step 4: Learn Exploratory analysis in Python using Pandas
Data can be explored more in detail with the help of the most important library known as Panda. Panda is used to read data sets and perform exploratory analysis to solve any problem.
In order to begin with data exploration, first of all choose the environment you want to work in. You can choose any of the above environments that we discussed in the above discussion. After selecting the appropriate environment, import the libraries you want and read the dataset.
After you read the dataset, go through the top rows of the dataset. You can also view more rows by printing the dataset.
Describe function is used to view the summary of numerical values. The function also offers count, mean value, standard deviation, quartiles etc in the output. If you wish to view non-numerical values, you can view frequency distribution for more detailed knowledge.
Step 5: Data Munging with Python
While our exploration of the data, we found a few problems in the data set, which needs to be solved before the data is ready for a good model. This exercise is typically referred to “Data Munging”.
Sometimes problems arise when there are some missing values in some of the variables. The missing values need to be estimated honestly so as to fill the missing spaces according to the expected values of variables.
Sometimes some datasets include extreme values that need to be adjusted appropriately.
To look into the missing values, you need to have inputting done because models with the missing number of values don’t work.
In order to fill the missing values, one must reconsider the estimated value by approaching the nearby values. Let us consider that the value of the loan amount is missing in the model. Now by taking a hypothesis such as if a person is educated or employed, he is able to give an estimated amount of loan.
We can also make a predictive model that will help to make data helpful for modeling. Some of the most important libraries used in creating datasets for a good model are Skicit-Learn. All the values need to be numeric if you are using this library. If not, the library will automatically convert all the variables into numeric values by encoding.
Step 6: Practice, Practice, and Practice
Once you gather all the knowledge and technical skills of using Python, what you all need is get a deep study of the terms and techniques being used in Python. GO through all the Python libraries, data structures and functions and practice each of them by your own implementation and coding. Practice more and more and you will be proficient in the programming language named as Python. Once you nailed it, you will get any data analyst job with a highly paid salary. Your skills required to be data analysts will be fulfilled once you learn Python with complete dedication.
The best way to practice your skills is to compete with your competitors and fellow data scientists via live competitions and search for other great ways to practice and excel in Python.
Try to solve as much Python tutorial questions as you can and use all the brain to solve those brainstorming questions. This will lead to get you more knowledge about the concepts as well as help you get some new things in your pocket. Keep your brain working on solving problems and coding.
Once you excel in data analysis, you will be counted among the top IT professionals of the times. Any company will be happy to pay you high amounts of salary if they see your technical skills in data analysis. Data analysis is the running course in the IT filed nowadays and getting efficient in it makes you the most wanted IT professional and expert in the market. Now is the time when big data is used worldwide on a large extent by almost all the IT companies. To handle those enormous data, companies need data analysts who are efficient in analyzing data and providing appropriate solutions to their problems as well as ways to boost the businesses.
The data analysts who have deep knowledge about Python have complete knowledge about data sets and data structures and are capable enough to get any data analytics job in any renowned company.