- Data Science Tutorial
- Data Science - Home
- Data Science - Getting Started
- Data Science - What is Data?
- Data science - Lifecycle
- Data Science - Prerequisites
- Data Science - Applications
- Data Science - Machine Learning
- Data Science - Data Analysis
- Data Science - Tools in Demand
- Data Science - Careers
- Data Science - Scientists
- Data Scientist - Salary
- Data Science - Resources
- Data Science - Interview Questions
- Data Science Useful Resources
- Data Science - Quick Guide
- Data Science - Useful Resources
- Data Science - Discussion
Data Science - Scientists
A data scientist is a trained professional who analyzes and makes sense of data. They use their knowledge of data science to help businesses make better decisions and run better. Most data scientists have a lot of experience with math, statistics, and computer science. They use this information to look at big sets of data and find trends or patterns. Data scientists might also come up with new ways to collect and store data.
How to become a Data Scientist?
There is a big need for people who know how to use data analysis to give their companies a competitive edge. As a data scientist, you will make business solutions and analytics that are based on data.
There are many ways to become a Data Scientist, but because it's usually a high-level job, most Data Scientists have degrees in math, statistics, computer science, and other related fields.
Below are some steps to become a data scientist −
Step 1 − Right Data Skills
You can become a Data Scientist if you have no data-related job experience, but you will need to acquire the necessary foundation to pursue a data science profession.
A Data Scientist is a high-level role; prior to attaining this level of expertise, you should acquire a comprehensive knowledge foundation in a related topic. This might include mathematics, engineering, statistics, data analysis, programming, or information technology; some Data Scientists began their careers in banking or baseball scouting.
But regardless of the area you begin in, you should begin with Python, SQL, and Excel. These abilities will be important for processing and organizing raw data. It is beneficial to be acquainted with Tableau, a tool you will use often to build visuals.
Step 2 − Learn Data Science Fundamentals
A data science boot camp might be a perfect approach to learn or improve upon the principles of data science. You can refer Data Science BootCamp which has each and every topic covered in detail.
Learn data science fundamentals such as how to gather and store data, analyze and model data, and display and present data using every tool in the data science arsenal, such as Tableau and PowerBI, among others.
You should be able to utilize Python and R to create models that assess behavior and forecast unknowns, as well as repackage data in user-friendly formats, by the conclusion of your training.
Several Data Science job listings state advanced degrees as a prerequisite. Sometimes, this is non-negotiable, but when demand exceeds supply, this increasingly reveals the truth. That is, proof of the necessary talents often surpasses credentials alone.
Hiring managers care most about how well you can show that you know the subject, and more and more people are realizing that this doesn't have to be done in the traditional ways.
Data Science Fundamentals
Collect and store data.
Analyze and model the data.
Build a model that can make prediction using the given data.
Visualizing and presenting data in user-friendly forms.
Step 3 − Learn Key Programming Languages for Data Science
Data Scientists use a variety of tools and programs that were made just for cleaning, analyzing, and modeling data. Data Scientists need to know more than just Excel. They also need to know a statistical programming language like Python, R, or Hive, as well as a query language like SQL.
RStudio Server, which provides a development environment for working with R on a server, is one of the most important tools for a Data Scientist. Another popular software is the open-source Jupyter Notebook, which can be used for statistical modeling, data visualization, machine learning, and more.
Machine learning is being used most in data science. This refers to tools that use artificial intelligence to give systems the ability to learn and get better without being specifically programmed to do so.
Step 4 − Learn how to do visualizations and practice them
Practice making your own visualizations from scratch with programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram. Find the best way to let the data speak for itself.
Excel is generally used in this step. Even though the basic idea behind spreadsheets is simple-making calculations or graphs by correlating the information in their cells-Excel is still very useful after more than 30 years, and it is almost impossible to do data science without it.
But making beautiful pictures is just the start. As a Data Scientist, you'll also need to be able to use these visualizations to show your findings to a live audience. You may have these communication skills already, but if not, don't worry. Anyone can get better with practice. If you need to, start small by giving presentations to one friend or even your pet before moving on to a group.
Step 5 − Work on some Data Science projects that will help develop your practical data skills
Once you know the basics of the programming languages and digital tools that Data Scientists use, you can start using them to practice and improve your new skills. Try to take on projects that require a wide range of skills, like using Excel and SQL to manage and query databases and Python and R to analyze data using statistical methods, build models that analyze behavior and give you new insights, and use statistical analysis to predict things you don't know.
As you practice, try to cover different parts of the process. Start with researching a company or market sector, then define and collect the right data for the task at hand. Finally, clean and test that data to make it as useful as possible.
Lastly, you can make and use your own algorithms to analyze and model the data. You can then put the results into easy-to-read visuals or dashboards that users can use to interact with your data and ask questions about it. You could even try showing your findings to other people to get better at communicating.
You should also get used to working with different kinds of data, like text, structured data, images, audio, and even video. Every industry has its own types of data that help leaders make better, more informed decisions.
As a working Data Scientist, you'll probably be an expert in just one or two, but as a beginner building your skillset, you'll want to learn the basics of as many types as possible.
Taking on more complicated projects will give you the chance to see how data can be used in different ways. Once you know how to use descriptive analytics to look for patterns in data, you'll be better prepared to try more complicated statistical methods like data mining, predictive modelling, and machine learning to predict future events or even make suggestions.
Step 6 − Make a Portfolio that shows your Data Science Skills
Once you've done your preliminary research, gotten the training, and practiced your new skills by making a wide range of impressive projects, the next step is to show off your new skills by making the polished portfolio that will get you your dream job.
In fact, your portfolio might be the most important thing you have when looking for a job. If you want to be a Data Scientist, you might want to show off your work on GitHub instead of (or in addition to) your own website. GitHub makes it easy to show your work, process, and results while also raising your profile in a public network. Don't stop there, though.
Include a compelling story with your data and show the problems you're trying to solve so the employer can see how good you are. You can show your code in a bigger picture on GitHub instead of just by itself, which makes your contributions easier to understand.
Don't list all of your work when you're applying for a specific job. Highlight just a few pieces that are most relevant to the job you're applying for and that best show your range of skills throughout the whole data science process, from starting with a basic data set to defining a problem, cleaning up, building a model, and finding a solution.
Your portfolio is your chance to show that you can do more than just crunch numbers and communicate well.
Step 7 − Demonstrate Your Abilities
A well-done project that you do on your own can be a great way to show off your skills and impress hiring managers who might hire you.
Choose something that really interests you, ask a question about it, and try to answer that question with data.
Document your journey and show off your technical skills and creativity by presenting your findings in a beautiful way and explaining how you got there. Your data should be accompanied by a compelling narrative that shows the problems you've solved, highlighting your process and the creative steps you've taken, so that an employer can see your worth.
Joining an online data science network like Kaggle is another great way to show that you're involved in the community, show off your skills as an aspiring Data Scientist, and continue to grow both your expertise and your reach.
Step 8 − Start Applying to Data Science Jobs
There are many jobs in the field of data science. After learning the basics, people often go on to specialize in different subfields, such as Data Engineers, Data Analysts, or Machine Learning Engineers, among many others.
Find out what a company values and what they're working on, and make sure it fits with your skills, goals, and what you want to do in the future. And don't just look in Silicon Valley. Cities like Boston, Chicago, and New York are having trouble finding technical talent, so there are lots of opportunities.