Best Practices for Data Science

What are the best practices for data science? What is the data science secret that separates good data scientists from great ones? These questions have been discussed by many, but in this article, we’ll use insights from some of the best and brightest minds in the field to put an end to these discussions once and for all. Here you’ll find data science best practices that will serve as your roadmap to achieving true mastery of this skill. So let’s start.

Contents

Sort out what you want to learn: 1

Set your skill in any programming language: 2

Learn data cleaning, data analysis, and data visualization by using Pandas: 2

Learn Statistics: 2

Learn Machine Learning (Supervised and unsupervised) 3

Take part in Kaggle Competition. 3

 

 

Sort out what you want to learn:

Data science is a vast field. You have heard with many peoples that you cannot find any jobs until you got 5-10 years of experience along with this that you cannot be a data scientist until you do not know statistics, linear algebra, calculus, programming, databases, machine learning, visualizations, analysis clustering, deep learning, natural language processing, and another storytelling. This simply is not the case all time.

 

So, what is data science, exactly? It is the process of finding useful insights from the data set and preparing intelligence questions that will help to grow the business and find the answers with the help of the insights. The data science work cycle looks like this:

 

  • Ask questions
  • Gather information that will help you in answering that question.
  • Clean up the data
  • Data exploration, analysis, and visualization
  • Train the data and then test the data set by using a machine learning model
  • Results should be communicated

 

Advanced mathematics, deep learning knowledge, and many of the other skills stated above are not necessarily required for Data Science. However, it does necessitate familiarity with a programming language as well as the ability to work with data in that language. Have strong communication and storytelling skills.

 

Set your skill in any programming language:

R and Python are both excellent choices for data science. R is popular in academics whereas Python in the industry, both languages include a large number of tools that help the data science workflow.

 

To get started, you do not need to know both Python and R. Instead, concentrate on grasping a single language. If you have decided on Python you can start with installing the Anaconda, which makes package installation and administration easier on Windows, OSX, and Linux.

 

You also do not need to be an expert in Python.

  • Data types (string, int, float, etc.)
  • Data structures (dictionaries, tuples, lists)
  • Functions
  • Conditional statements (if, elif)
  • Comparisons loops (while loop, for each, for)

 

In my opinion, these are the basic topics which you need to concentrate on more.

 

Learn data cleaning, data analysis, and data visualization by using Pandas:

 

After learning python basics now you have to focus on the pandas library. In Python pandas library, uses for data manipulations. Similar to an Excel spreadsheet or SQL table, pandas provides a high-performance data structure (called a "Data Frame") that is ideal for tabular data with columns of various sorts. It has tools for reading and publishing data, dealing with missing data, filtering data, cleaning up untidy data, merging datasets, visualizing data, and much more. In short, mastering pandas will dramatically improve your data-processing efficiency.

 

Learn Statistics:

 

Statistics is a branch of science that deals with collecting, analyzing, and interpreting data to describe and predict the facts of our world. Data science is heavily used statistics in it. So you must gain knowledge about statistics before learning data science. 

It is divided into two categories.

1.     Descriptive statistics- It provides strategies for summarizing data by converting raw observations into understandable and shareable information i.e. (min, max, standard deviation, variations, etc.)

2.     Inferential statistics- It provides strategies for analyzing tests conducted on small samples of data and drawing conclusions for the total population (entire domain).

So in my opinion before starting machine learning you have to get knowledge about statistics first then move towards Machine learning to understand the algorithm’s functionality theoretically.

           

Learn Machine Learning (Supervised and unsupervised)

Now you are starting Machine learning?  You should learn how to use the sci-kit-learn library in Python for machine learning. The main part of data science is creating "machine learning models" to automatically extract insights from data. For good reason, sci-kit-learn is the most popular machine learning library in Python. If we talk about the theory of machine learning. Mainly it is classified into three parts

 

Supervised Learning: ML algorithms that train on labeled data are known as supervised learning. It means that we are supervising machines by providing the dataset in which both the input and the output variables are defined.

Unsupervised learning: ML algorithms that train on unlabeled data are known as unsupervised learning. The algorithm looks for an appropriate relationship between data sets. The data used to train algorithms, as well as the forecasts or suggestions they produce, are all predetermined.

 

 Reinforcement learning is a technique used by data scientists to train a machine how to finish a multi-step process with well-defined rules.

So you have to take almost 3-4 months on learning ML after learning this my opinion is to do any machine learning projects it will help you to get more grasp on this field.

 

Take part in Kaggle Competition

My advice for enhancing your data science skills is as follows:

Find "the thing" that motivates you to put what you've learned into practice and to continue learning, and then do it on Personal data science projects, Kaggle competitions, online courses, reading books, reading blogs, visiting meetings or conferences, workshops, webinars these are the things that will help you to grow your knowledge.

 

Now the time comes, when you have learned python, statistics, data analysis, machine learning, and did some real-time projects. Start taking part in the Kaggle competition so that you can get experience.

 

Kaggle competitions are an excellent way to get some data science practice without having to come up with your problem. Don't be concerned about your ranking; instead, concentrate on learning something new with each competition. (Keep in mind that you won't be able to practice key aspects of the data science workflow, such as asking questions, acquiring data, and conveying conclusions.)

 

You can gain experience collaborating with others by contributing to open-source projects. GitHub is the platform where not only you can view others' projects but also you can collaborate with others.

 

You should share your data science projects on GitHub and also you can start your blogs to gain knowledge and can share with others if you prepare. This will demonstrate to others that you are capable of reproducible data science.

 

Hope that this blog will help full for you and by reading this blog you will get to know how you can do practices for a Data Science career.

 

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