Step By Step Guide How to Become a Data Scientist in 2024 | What skills are needed
Step By Step Guide How to Become a Data Scientist – What skills are needed to be a Data Scientist. 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.
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.
Steps to Become a Data Scientist
The general steps to becoming a data scientist:
- Earn a bachelor’s degree in IT, computer science, math, physics, or another related field.
- Earn a master’s degree in data or related field.
- Gain experience in the field you intend to work in (ex: healthcare, physics, business).
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.
Step By Step Guide On How To Become A Data Scientist
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.
STEP 2. 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.
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.
STEP 4: 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.
STEP 5: 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.”
STEP 6: 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 Data Manipulation, Data Analysis using Pandas and Numpy, Data Visualization.
Step 7: Practice
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.
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Can a fresher become data scientist?
There is a lot of doubts among the fresh candidates with or without analytics training regarding their acceptance into the data science industry. Good news – that you cannot become a data scientist right away does not mean that you cannot become a data scientist ever.
