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Guide To Learn Data Science From Scratch

28 Oct 2022
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It’s no secret that data science is the most popular field in the world. It involves a lot of information and research. This unique career field is loaded with bright options. Data science involves a lot of technical knowledge it’s also a visionary creative field where innovative and creative thinking gets a way out for deep meaningful business insights.

In this article let’s learn the aspects of data science and see how we can get to learn data science from the scratch. The Data Science life cycle is very interesting with many technical fields. Various techniques like data analysis, data mining, business analysis, predictive modeling, data modeling, machine learning, etc. play an amazing part in the formation of the data science lifecycle.

Why Is Data Science In Demand?

Data Science is adding gems to almost all the businesses we have around. Its exceptional results have made every industry want to apply data science methods in business. 
With the bundle of data that is constantly generated from customers, the companies study and get useful information from the data which is used for the business benefit. It helps companies to get amazing results and make the best business decisions in the interest of the company.

Data science applications are used in almost all business fields. Some of the fields that use data science are banking, healthcare, retail, marketing, the corporate sector, entertainment, security, logistics, sales and supply, management, and many more. You get the opportunity to explore your desired career field and specialize in data science accordingly. There are multiple career options and bright prospects.

If you are looking for a career in data science, you will find the information below quite interesting. The demand for data scientists is an all-time high. If you want to switch or start your career in Data Science, enroll in Data Science Course!

The Most Adequate Ways To Learn Data Science Are:

Discover The Reason To Learn Data Science

Data science is a vast field with plenty of possibilities to learn the new program. There is ample data available that can be utilized in several fields. It’s you, who has to decide what field you would like to specialize in. for example; if you have a great interest in statistics you can choose business analytics or banking and explore various possibilities in statistics. 

With the correct knowledge and right guidance, you can achieve huge success in any data science field.

Make Sure Your Fundamentals Are Right.

You must know that before jumping on the expert areas make sure your basics are clear. It's always exciting to learn technical stuff like machine learning, image recognition, data visualization, or neural networks, but it's very important to know that the most essential task for any data scientist is to know data cleaning. 

So if you are good at data cleaning rest all becomes an easy road ahead.

Before investing your time in the advanced features learn the basics and get the right direction to your goal. Learn clustering, linear and logistic regression. Also, see to it that your project concept is all clear. Building a portfolio and working on the project are the main elements of data science; here you know all the challenges and key points as to how to handle the project with clarity. 

Express & Communicate

The data scientist has to submit data reports and business insights very frequently. The task should be done with great precision and ease to delivery. The report or data submission becomes valuable if presented rightly. It is important to make sure that your message has been conveyed properly to the client.

Communication is dependent on the most important aspects and they are:

  • Understand your subject.
  • Make sure your data is well maintained, recorded, and arranged.
  • The simple and prominent explanation of your analysis.
  • The best way to communicate with the client is to write a blog and explain your data analysis. 
  • You must train your team who needs more attention in computer science. By teaching them, your knowledge will sharpen and your concepts will be clearer.
  • Use GitHub to share your analysis report.

Collaboration

Teamwork is the best form of work. As a data scientist, you will be coordinating with various groups regarding information exchange and work report analysis. 

  • Data science is teamwork so the more comfortable you are working in a team; the more your job will become more result oriented.
  • Contribute open source programs, and patch up with the team.
  • Try to connect with the writers or data scientists who write data analysis blogs and collaborate to learn from them.

Accept The Challenge

Data science is such a huge stream the more you learn there is more to explore. The more you experience the more you will be valuable in this process.

  • You must level up your skill set.
  • Work with higher data sets. 
  • Practice more on algorithms 
  • Test your algorithm with various processors 
  • Improve your understanding of the algorithm and the theory behind it.
  • And lastly the most interesting challenge you will always find beneficial.

Teach And Learn Twice 

By doing that not only you will be clearer with your concepts but will also get more confident. The best experience and deeper knowledge will be all you will get back. 

What are the prerequisites to learning data science?

The various online programs or tutorials, and practical courses teach data science from the scratch. Right from the basics of computer fundamentals likes mathematics, algorithm, and statistics to programming languages with python/R, SQL, etc.
A clear knowledge of maths and the concept of integration will be helpful.
The statistics with probability department will become fundamental when you get a detailed course.
Knowledge of at least one programming language, data structures, and OOP programming will be a good help.

Roles & Responsibilities

Some of the key roles of a data scientist are:

  • Work with stakeholders to understand the business issues.
  • Rectify the problem, prepare the structure and collect the data accordingly.
  • Verify the accuracy of data, clean the data accordingly and extract it into meaningful information.
  • Find various data patterns and create visualizations to perform data analysis.
  • Build algorithms and data models and figure out the accuracy of models.
  • Evaluate the end result and acquire solutions as per the feedback.

What Are The Steps Of Learning Data Science?

Make your basics strong in Math and Statistics

Math and statistics are the base and the strength of data science. They are the foundation of the data science field. If you are into data science make sure your calculations and probability are up to the mark. Get the basics right, learn from the beginning at fingertips you will get detail on each data science program.

Here is a video that will guide you to learn the basics of data science.




Learn Python and R programming

Python and R are the most favored programming languages in data science. Along with math concept fundamentals, it’s also necessary to have a good understanding of the programming language. Python and R are open-source programs and anyone interested to learn the program can go for them. The syntax of both is easy to use. They are compatible with Linux, Windows and macOS platforms.
Almost any task can be conveniently pulled up by python and R. When you have to handle extensive data python is the right tool. All the deep learning tasks like web scraping and workflow automation are performed better by Python than compared to R. 

When it comes to statistic handling R is the language you can rely on. It has a great collection of statistical languages which you can apply to data sets instantly. R is the best tool for building statistical models compared to python. 
If you are aspiring for deep learning or artificial intelligence Python is the best tool for beginners. You can start with R if you are more interested in statistics and model building.  

Introduction To The Databases

A data scientist needs to understand and be able to work with databases. Databases are used for data retrieval and data storage.
SQL (Structured Query Language) is the most preferable database language. It allows you to store data, modify the data, and create tables and graphs. It’s an advantage that some big data tools have extensions that allow you to make queries using SQL.
As a data scientist, it’s okay not to have detailed database knowledge. As those particular tasks are performed by database engineers. But it’s good to be familiar with the basics of SQL like data storage and retrieval.

Learn Data Analytical Program

As a data scientist, you must be open to various problem-solving techniques when it comes to data analysis. It is the very important job of the data scientist to have foresight as to which data solutions can be provided to any particular issue. 
Cluster analysis, regression system analysis, and cohort analysis are a few techniques that data scientists usually used.   
The best data analyst is one who is quick in detecting which data analysis technique is required to relate to any particular problem. This application of the right technique to the problem comes with experience and practice. 

Plan, Practice & Perform

After having a good understanding of data analytics, just begin to try your hand at projects that apply all the methods you have learned. It is very important to know the strong fundamentals of programs than to have overall information about everything. Dig down deeper and understand the methods to apply to data analytics. 
Make sure that practically you implement python programs or R programming in any statistical issue. Practice well, as only a better understanding of the data solution will give you a wider experience and better prospects.

How to Use Data Science Tools?

Many tools in data science perform different functions. The most popular tools are BigML, D3, MATLAB, Excel, ggplot2, Tableau, etc
It is essential to have good knowledge of and functions of each data science tool. It makes your concept clear as to which particular tool should be used for certain data analytical problems or processes.
At this stage specialization isn’t very necessary, just knowing every tool for study and practice is a good start. The main purpose should be to have a clear idea as to which situation and what tool must be used. 

Data Science Projects

Everything that you have learned just binds it up into a project. There are different types of projects. Check out how you can go about it.

Sentiment Based Analysis

This type of project requires sentiments to be attached to any particular text. You can choose the either binary or granular approach and react to the text in various sentimental ways. Like happy, sad, excited, etc.
This type of sentiment-based analysis is usually applied on social media. The feeds are a great source of data and can be used in various ways for the company's benefit. With the right implementation of a hashtag on the project, you can analyze it accordingly.

Recommendation Analysis

For example, you have to promote any movie or series on the internet, so there are particular data sets you need to use and make sure that the recommendation program is done properly. All the data that includes actors, subject, runtime, etc is used for the recommendation program.

Communication And Good Presentation Skills

Data scientists need to convey the analysis report clearly so it becomes easy for the client to understand the organics of insights. A good analysis report if not presented in order can create a wrong impression about the report's perspective.
Data that is analyzed should be presented well in an organized way for easy understanding. Communication plays a very important role to line up between the client and their expectation.
The good use of graphics, graphs, reports, charts, and videos should make magic. It will be an easy route toward understanding the audience.

Is it Possible to Learn Data Science on Our Own?

There are many options on the online platform where with the right sources you can learn data science. Through videos and tutorials, there is the possibility to explore the subject. The lack of the right procedure may affect the understanding. Also, the self-learning process can make you miss important key points or elements. It is still believed that getting the right lessons from the right resources is always the best source of gaining knowledge. With experienced tutors and practical assignments, your knowledge will be a quality experience. At fingertips, you get a detailed course structure with a practical exploration of a subject.
Fingertips Data Science Program is designed according to the needs of today's market. Framed under the expert observations of years of experienced Data Science Experts, the program offers an innovative platform to explore the hidden possibilities in the era of Data Science.

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 fingertips Fingertips

Fingertips is one of India's leading learning platforms, enabling aspirants - working professionals, and students to enhance competitive skills and thrive in their careers. We offer intensive training in areas such as Digital Marketing, Data Science, Business Intelligence, Artificial intelligence, and Machine Learning, among others.

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