How to become a Data scientist?

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Data science is arguably the hottest career of the 21st century. In today’s high-tech world, everyone has pressing questions that must be answered by “big data”. From businesses to non-profit organizations to government institutions, there is a seemingly-infinite amount of information that can be sorted, interpreted, and applied for a wide range of purposes. Read on to learn how to become a data scientist and jump onto this booming career path!

Finding the right answers, however, can be a serious challenge. How can a business sort through purchasing data to create a marketing plan? How can government departments use patterns of behavior to create engaging community activities? How can a non-profit best use its available marketing budget to further enhance its potential operations?

It all comes down to data scientists.

Because there is simply too much information for the average person to process and use, data scientists are trained to gather, organize, and analyze data, helping people from every corner of industry and every segment of the population.

Data scientists come from a wide range of educational backgrounds, but the majority of them will have technical schooling of some kind. Data science degrees include a wide range of computer-related majors, but it could also include areas of math and statistics. Training in business or human behavior is also common, which bolsters more accurate conclusions in their work.

There is a nearly infinite amount of information, and there is a nearly infinite amount of uses for data scientists. If you are intrigued by this captivating work, then let’s take a closer look at the career as a whole. Explore what they do, who they serve, and what skills they need to get the job done.

What is a Data Scientist?

Data science is a complex and often confusing field, and it involves dozens of different skills that make defining the profession a constant struggle.

Essentially, a data scientist is someone who gathers and analyzes with the goal of reaching a conclusion. They do this through many different techniques. They may present the data in a visual context, which is often called “visualizing the data,” allowing a user to look for clear patterns that wouldn’t be noticeable if the information was presented in hard numbers on a spreadsheet. They often create highly advanced algorithms that are used to determine patterns and take the data from a jumble of numbers and stats to something that can be useful for a business or organization. At its core, data science is the practice of looking for meaning in mass amounts of data.

Let’s look at a fairly typical example of a data scientist in action. Perhaps a major business, say a cell phone company, wants to know what current customers are more likely to switch services to their competitor. They may hire a data analyst who can look at millions of different data points (or more specifically, create an algorithm to look at millions of data points) related to former customers. They may discover that customers who use a certain amount of bandwidth are more likely to leave, or that customers who are married and between the ages of 35 and 45 are the most likely to switch carriers. The cell phone company can then change its business plan or marketing efforts to engage and retain these customers.

Netflix users see a real-world example of data management in action every time they access their accounts. The video streaming service has a program designed to give you suggestions that will best fit your preferences. Using information from your past viewing history, an algorithm gives you recommendations for shows you may enjoy. This is also seen in services like Pandora with their thumbs-up and thumbs-down buttons, and from Amazon, with their shopping recommendations.

There are tons of resources and links out there, but often we get confused on which resources to follow. Don’t worry, I have got you covered. I have attached the links to several YouTube channels, blogs, courses, and other websites that I found appropriate for a beginner.

You can also use the Data Science Community Websites Like Analytics Vidhya and Kaggle for implementing your learning and getting hands-on experience in Data Science.

STEP 1: Choose A Programming Language (Python / R)

The first step while starting the Data Science Journey is to get familiar with a programming language. Between the two, Python is the most preferred coding language and is adopted by most Data Scientists. It is easy to understand, versatile, and supports various in-built libraries such as Numpy, Pandas, MatplotLib, Seaborn, Scipy, and many more.

  1. FreeCodeCamp’s Python Tutorial (Recommended)
  2. Kaggle’s Python Course
  3. Krish Naik’s Python Tutorial (Recommended)
  4. Udemy’s Python for Data Science and Machine Learning Bootcamp
  5. Coursera Python Course

NOTE: While learning Python, one should know essential Python variables, data types, OOPs concepts, Numpy, Pandas, Matplotlib, and Seaborn.

STEP 2. Statistics

For becoming a Data Scientist, having knowledge of statistics and probability is as essential as having salt in food. Knowing them will help the data scientists interpret large data sets, get insights from them, and analyze them better.

  1. Krish Naik’s Statistics Playlist (Recommended)
  2. Coursera Statistics Course
  3. Khan Academy Statistics And Probability Course
  4. FreeCodeCamp Statistics Course (Recommended)

NOTE: Statistics provides the ideas about Mean, Median, Mode, Range, Variance, Standard Deviation, Graphs or Plotting, Populations, and Samples.

STEP 3: Learn SQL

Structured Query Language (SQL) is used for extracting and communicating with large databases. One should focus on understanding the different types of normalization, writing nested queries, using co-related questions, group-by, performing join operations, etc., on the data and extract in raw format. This data will then further be cleaned either in Microsoft Excel or by using Python libraries.

  1. Freecodecamp SQL (Recommended)
  2. Intro To SQL By Kaggle (Recommended)
  3. Advanced SQL By kaggle
  4. Edureka’s SQL Playlist

NOTE: In SQL, one should know about creating tables, inserting data, updating data, deleting data, and performing some basic query operations.

STEP 4. Data Cleaning

When a Data Scientist is given a project, the majority of the time goes into cleaning the data set, removing unwanted values, handling missing values. It can be achieved by using some inbuilt python libraries like Pandas and Numpy.
One should also know how to manipulate data using Microsoft Excel.

  1. Blog — Cleaning Data Using Python (Recommended)
  2. Edureka’s Microsoft Excel Course
  3. Learning Pandas By Kaggle (Recommended)

NOTE: In Microsoft Excel, you should know basic data filtering or sorting, Functions or Formulas, Vlookup, Pivot table and charts, and Tables, etc.

STEP 5: Exploratory Data Analysis

Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks, including finding data patterns, analyzing data, finding the appropriate trends in the data and obtaining valuable insights, etc., from them with the help of various graphical and statistical methods, including:

A) Data Analysis using Pandas and Numpy
B) Data Manipulation
C) Data Visualization

  1. Intro To EDA By Code Heroku’s (Recommended)
  2. Blog — Performing EDA on Iris Data Set (Recommended)
  3. Coursera Course On EDA, Statistics, Probability
Data Scientist

Types of plots in the Seaborn Python library.

STEP 6: Learn Machine Learning Algorithms

According to Google, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

It is the most crucial step in a life cycle of a data scientist where one has to build various models using machine learning algorithms and should be able to predict and come with the most optimum solution to solve any problem.

  1. Machine Learning By Andrew NG (Recommended)
  2. Deep Learning By Krish Naik
  3. Intro To ML By Kaggle (Recommended)
  4. Machine Learning By Krish Naik (Recommended)
  5. Coursera Deep Learning Specialization
Data Science

Machine Learning landscape.

Step 7: Practice On Analytics Vidhya and Kaggle

After acquiring the basics of Data Science, now it’s time to get hands-on experience in its part. There are many online platforms, like Kaggle and Analytics Vidhya, that can provide you with hands-on experience with both beginner and advanced level data sets. They can help you to understand various machine learning algorithms, different analyzing techniques, etc.

You can follow the below approach to know how effectively you can use these platforms.

  1. You can start by first downloading the datasets and analyzing the data, and implementing all the different techniques you have learned.
  2. Next, you can check on other people’s notebooks and understand how they have solved a particular problem or gained insights from the data. (This method will certainly make you more confident and help to improve your knowledge.)
  3. After you are confident enough, you can participate in Competitions organized by both Kaggle and Analytics Vidhya. This will not only help you to sharpen your skills in Data Science but also to learn Data Science better.

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