- 1 What is Data Science?
- 2 How to Become a Data Scientist?
- 3 Different Types of Profile in Data Science
- 4 The Important skills for Data Science?
- 5 Where Should I learn Data Science and Related skills?
- 6 Why Data Science is Very Demanding?
- 7 Which is the No Code or Less Coding Work?
- 8 Conclusion
In this blog, we are going to learn how to choose the right career in data science or how to start a career in data science as a fresher.
Before knowing how to choose data science as a career everyone should understand what is your quality first, like what are the skills you have or what are the things you most like.
For example, if you love to solve problems or you are good at logic, and programming or you have a very analytical mindset then based on your expertise and interest you can choose your career.
Because every profession requires expertise and consistency in work to become good in a specific career to achieve more growth, money, and recognition.
In any work or field, there are different branches and micro niches which you can choose and master in that because learning everything and getting a master’s in that is a very time-consuming and complex task.
Below I will show you what, how, and where you can choose and learn about data science and the related skills to grow in a specific area.
What is Data Science?
As a student, you may be wondering what data science is and what it can do for you, It is the process of extracting knowledge and insights from data.
It can be used to improve decision-making, predict future events, and understand complex processes, Data science is a growing field, and there are many opportunities for students who want to pursue a career in data science.
To pursue a career in data science, you will need to have strong skills in math and statistics, as well as in computer programming, You will also need to be able to extract meaning from data, which requires strong analytical skills.
The best way to learn data science is by observation and the daily practice of code and data, There are many online tutorials and courses available that can teach you the basics of data science.
There are also many competitions and challenges available that will allow you to practice your skills in data science.
If you want to pursue a career in data science, it is important to research the different jobs available and find one that matches your skills and interests.
How to Become a Data Scientist?
The field of data science is growing rapidly, and If you are interested in data science, there are several things you can do to prepare yourself for a career in the field.
1. First, you should learn how to use data mining and statistical analysis tools.
2. You should also be familiar with programming languages such as Python and R.
3. In addition, you should develop your problem-solving skills and learn how to think critically.
4. Finally, it is also important to have a strong background in math and statistics.
If you want to learn more about data science, there are several resources available to you, The best way to learn is by doing.
There are many online tutorials and MOOCs (massive open online courses) that can help you learn the basics of data science.
There are also many books on data science that can help you understand the concepts involved in the field.
Finally, there are many data science conferences and meetups where you can learn from experts in the field.
Different Types of Profile in Data Science
To start a career in data science there are different types of job profiles available where you can choose your suitable one as per your expertise.
All profiles are based on industry and skills and these profiles divide data science or analytics into different categories like below.
1. Data Scientist
This profile is highly dependent on end-to-end data analysis, data modeling, statistical analysis, and predictive modeling of data.
- Here you need to gather data from different sources using data collection techniques,
- Then Need to perform data cleaning, preprocessing, Exploratory Data Analysis, and feature engineering.
- After getting the data in the right form then you can perform predictive modeling on it.
- You can perform Natural language processing, Computer vision, or other Deep learning operations on data also.
2. Data Analyst
Data analysis is the traditional analysis which is mostly the previous version of data science but still, it has different usability based on the problem.
A data analyst works on data to find useful business insights to solve any business problem efficiently.
They also do communication with company leadership and other stakeholders about the business insight information.
Data analysis starts with data gathering (Collection) and ends with data reporting and visualization to get the right insight from the data.
3. Data Engineer
Data engineering is a highly demanding area in the cloud industry where there is more demand for data engineers for complex data operations.
Data engineer works at the backend where they need to create a system where data can be correctly extracted, transform, and load.
Data engineers mostly need data language SQL and scripting languages like python/scala for data engineering work.
4. Machine learning Engineer
If you’re thinking of becoming a machine learning engineer, then you need to focus on specific things like maths and coding. This field is growing rapidly, and there are plenty of opportunities for career growth.
Machine learning is a field of computer science that enables computers to learn from data without being explicitly programmed.
Machine learning algorithms can automatically improve with experience, making them ideally suited for tasks such as predictive modeling and pattern recognition.
Machine learning engineers Required a strong understanding of computer science principles, including algorithms, data structures, and software engineering.
In addition, you need to have a basic understanding of the concepts of statistics and probability, A background in artificial intelligence is also beneficial.
5. Data Consultant
A data consultant is someone who helps businesses make sense of the data they have. They help to organize and analyze the data and then present it in a way that is easy to understand.
This can help businesses make better decisions about their products and services, If you are interested in becoming a data consultant, there are several things you can do to prepare yourself.
First, you should have a strong understanding of statistics and data analysis. You should also be familiar with different software programs that can be used to organize and analyze data, such as Excel and SQL.
Additionally, it is helpful to have a background in business or marketing, so you can understand how to use data to improve a company’s bottom line.
If you want to become a data consultant, you can start by getting a degree in statistics or data analysis.
You can also get a certification from a data analysis organization, such as the SAS Institute. These organizations offer courses and training that will help you become an expert in data analysis.
6. Cloud Architect
Cloud architecture is a complex and rapidly evolving field. As a cloud architect, you need to stay up to date on the latest trends and technologies to ensure your cloud infrastructure meets the needs of your business.
The skills need to have as Cloud Architect include cloud computing trends, cloud architecture, cloud security, big data, and more. You also need to how to find the right solution to get the most out of your cloud infrastructure.
One way to develop the skills you need as a Cloud Architect is to join a professional organization like (ISC)2, the Cloud Security Alliance, or the OpenStack Foundation.
7. Business analyst
A business analyst is a professional who helps organizations improve their performance by analyzing their business processes and recommending improvements.
the business analyst is responsible for identifying and assessing business needs and requirements; defining and documenting business processes; recommending process improvements; working with IT staff to design and implement new systems or enhancements to existing systems.
8. Financial Analyst
A financial analyst is a professional who helps companies make sound financial decisions, They work with data to forecast future trends and recommend solutions to financial problems.
Financial analysts must have a strong understanding of financial concepts and be able to communicate effectively with other professionals.
financial analysts are also like business analysts but they mostly work on financial data to provide the right decision to banks or financial institutes to make the right move related there investments and transactions.
9. Cloud Developer
Cloud Developer is an exciting and growing field, and if you’re looking to make a career in it, you should have skills like SQL, Scriptings like Python or scala, etc.
Cloud developers can do different types of cloud operations related to cloud storage, data pipeline, monitoring, security, and different type of streamlining processes on the cloud.
It is similar to the data engineer work, but they mostly need to work on cloud development and DevOps activities.
Similarly, they have the responsibilities to maintain the agile process with end-to-end solutions in a cloud environment.
10. Data Architect
A data architect is a professional who designs and oversees the construction of data storage systems.
They work with teams of engineers to ensure that the systems are efficient and reliable and that the data is accessible to the people who need it.
data architects work to create system data architecture, where organizations can easily design, create, deploy and manage the data efficiently.
The Important skills for Data Science?
To start a career in data science you need to focus on the below skills, as well as try to master a specific skill like programming to get your first job.
- SQL (RDBMS, T-SQL)
- Python (Pandas, Numpy, scikit-learn, Other)
- Basic Mathematics (Linear Algebra, Calculus, Statistics, probability)
- Data Analysis (EDA, Plotting, R, SAS)
- Machine Learning
- Big Data Technologies
- Data Visualization/Reporting (Tableau/PowerBI/Excel)
- Deep Learning/AI
- Cloud Computing (Azure/AWS/GCP)
Where Should I learn Data Science and Related skills?
There is no end to the data science resources you mostly get free resources on the internet to master the data science skills, and these skills can fulfill your question like how to start a career in data science.
- Coursera: Introduction to Data Science using Python
- Coursera: Machine Learning Specialization by Andrew Ng
Why Data Science is Very Demanding?
There is no end to these skills because there is a lot to learn in data science, Because Data is everywhere now, all businesses are using the internet for transactions and trading.
That is why a large amount of data is getting generated every day from customers or users.
Similarly, Data is not only generated by companies but also every client-server transaction creates data, that need to collect, stored, and analyzed.
For all-purpose, we need the right tool, techniques, and expertise to collect, store, and manages the data in the right way.
That is why different tools, technologies, and expertise are required to solve any problem efficiently with help of data.
That is the reason data science, data analysis, or data engineering is a very demanding skill in the software or It industry.
Which is the No Code or Less Coding Work?
If you are not good at coding not worry there are some other skills you can learn to grow in this field like Data Analysis, Data Visualisation, Business analysis, etc.
Data Analysis can be possible using Excel, Power BI, or Tableau-like reposting tools which are mostly used for structured or semi-structured data analysis.
These tools are very easy to learn and very high demanding in the industry, which works without any code or less code.
Similarly, like these tools, there are other data visualization or reporting tools available in the market like Qlikview, Google Data Studio, etc. that you learn based on requirements.
There is another skill called salesforce which is also no code or less code platform or framework used for cloud computing and CRM.
Salesforce is also the best way to solve any data-related problem easily using cloud support. And the profile for this job is salesforce admin or salesforce developer.
All the skills and reason makes data science the best career option for a computer engineering student and this blog has the complete answer on how to start a career in data science?
Similarly, data science is not only for computer students but it can be suitable for any student who is low to solve problems and good at coding.
To start a career in data science focus on learning the basics of data science, including data mining, statistics, and machine learning.
Get experience working with data by completing projects or participating in data science competitions like hackathons or Kaggle.
Network with data scientists and other professionals in the industry with help of your LinkedIn profile.
Stay up to date on the latest data science news and trends follow the articles, blogs, and news on different analytics platforms.
Nitin is a professional data Engineer, Who has a Post Graduation in Data Science and Analytics and working in the healthcare sector. Experts in Data analysis, Machine learning, AI, blockchain, Data related tools, and technologies. He is the Co-founder and editor of analyticslearn.com