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Jagpreet
28 May, 2024
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“It’s easy to lie with statistics. It’s hard to tell the truth without statistics.” -Andrejs Dunkels
Hello Readers,
I have a question for you! When was the last time you watched Netflix or listened to Spotify? You may have noticed that after a movie or song ends, you instantly receive a recommendation that matches your interests. This is not intuition; it is data science! They analyse vast amounts of user data, these algorithms learn your preferences and suggest content you are likely to enjoy.
Data science is a rapidly evolving field that bridges the gap between computer science, statistics, and business acumen. Data scientists are the wizards behind the curtain, the masterminds who unlock the secrets hidden within mountains of information.
According to the U.S. Bureau of Labor Statistics, the field of data science is set to expand significantly. From 2022 to 2032, employment for data scientists is expected to grow by 35%, which is much faster than the average for all occupations. On average, there are projected to be about 17,700 job openings for data scientists each year over the decade. Many of these openings will arise as workers switch careers or retire from the workforce.
Do you know how Netflix recommends shows to you, or how Spotify curates playlists that perfectly suit your taste? These are just a couple of examples of the magic data scientists weave behind the scenes. But who exactly is a data scientist, and what do they do?
A data scientist is someone who collects, analyses and interprets data to aid decision-making in a company. This role combines skills from various fields like Math, Science, Statistics, and Computer Programming. Data scientists use advanced techniques like (ML) machine learning and predictive modelling to find insights from data.
This is the first step that you have to take. Data scientists work in various fields such as collecting data from marketing to stock marketing. So, before you start training to be a data scientist, just pick a field that you are really interested in. Then, focus on developing your skills and expertise. Remember, having a specialisation also boosts your chances of finding a great job and getting paid well.
Here are some popular specialisations that you can check out:
In this specialisation, you will be learning about how you can use data to make business decisions and solve problems. If we talk about the key skills, it will include Statistical Analysis, Database Management, and Report Production. You will be amazed to know that after pursuing a career in this specialisation you can make your career in various roles such as Business Intelligence Analyst, Market Research Analyst, and Business Analyst.
In this specialisation, you will be learning about data systems, algorithms, and big data infrastructure. You will be developing skills in Data Science Tactics, Engineering, and Information Retrieval. And the great news is that after pursuing this specialisation you can go for various industries such as Retail, Healthcare, Defense, Finance, and Banking.
In this specialisation, you will be learning about managing big data warehouses and solve engineering problems. You will be learning skills such as optimising the Data Collection, Processing data and building models for large data sets. After pursuing this specialisation, you can make a successful career as Data Analyst, Data Engineer and Data Manager.
In this specialisation, you will be learning about how to design and manage big data systems. You will be learning precious skills such as Database Design, Data Warehousing, and Big Data Systems Engineering. You can even make your career as Data Engineer, Database Architect, and Data Administrator.
Now you have to choose a course that can help you pursue your dream career. When you have selected a course and area of specialisation, you can start searching for colleges that offer the same course with the specialisation required to excel in your area of interest.
You might be aware that this Data science industry keeps evolving every day. Therefore, you will get to see the new trends that keep on changing every day. So you do not have to stop exploring the world of data. You should stay up-to-date and ahead of the curve. You can get in touch with your peers to get insights into the latest trends in the data science field.
You can even pursue certificate courses to develop the skills and knowledge that are required to thrive in the field of data science. Many institutes offer data science certification courses that will increase the chance of getting high paying salary.
Here are Some Certificate Courses:
1. Meta Data Analyst Professional Certificate:
Provider: Meta
Duration: 5 months (10 hours per week)
Outcome: It will prepare you for an entry-level job as a Data Analyst.
2. Google Data Analytics Professional Certificate:
Provider: Google
Duration: 6 months (10 hours per week)
Outcome: It will prepare you for an entry-level job as a Data Analyst.
3. IBM Data Science Professional Certificate:
Provider: IBM
Duration: 3 months (12 hours per week)
Outcome: It will prepare you for an entry-level job as a Data Scientist.
4. IBM Data Analyst Professional Certificate:
Provider: IBM
Duration: 3 months (10 hours per week)
Outcome: It will prepare you for an entry-level job as a Data Analyst.
5. IBM Data Engineering Professional Certificate:
Provider: IBM
Duration: 7 months (3 hours per week)
Outcome: Prepares you for an entry-level job as a data engineer.
Practical experience is also valuable. You can get some practical experience by working on internships and real projects. You can use the platforms like Kaggle or GitHub to show your portfolio of your projects. These practical and hands-on experiences will definitely enhance your problem-solving skills and reinforce your understanding of data science concepts.
If you want to become successful in the data science field, make sure to improve your soft skills as well. You have to brush up on the skills such as communication, critical thinking, and teamwork abilities. These skills will help you to convey your point of view to your team. You will be able to share different viewpoints together to solve problems effectively.
It is necessary to understand the importance of Soft skills in Data science career, here are some of the Examples.
Communication:
You should know you can clearly explain complex technical concepts to non-technical stakeholders.
You should know how to write reports and create presentations that convey findings effectively.
Critical Thinking:
You should know how to analyse problems from multiple perspectives.
You should know how to evaluate the quality and relevance of data sources.
Collaboration:
You should know how to work well within a team.
You should know how to share insights and findings with colleagues to collectively solve problems.
Problem-Solving:
You should know how to develop innovative solutions to complex data challenges.
You should know how to apply logical reasoning to identify and address data-related issues.
Time Management:
You should know how to balance multiple projects and deadlines efficiently.
You should know to prioritise tasks to manage workload effectively.
Now this is the final step. You should start crafting a resume that showcases all your areas of expertise and skills. Then you will be able to find the job that matches your interest.
There are some popular platform that can help you to land a successful career.
| Platform | Features and Benefits |
| – Extensive network of professionals and companies. – Job postings specifically for data science roles. – Opportunity to connect with industry leaders and join relevant groups. |
|
| Indeed | – Wide range of job listings from various companies. -Advanced search filters to narrow down roles specific to data science. – Option to upload your resume and get job recommendations. |
| Glassdoor | – Job postings along with company reviews and salary information. – Insights into company culture and employee experiences. – Useful for researching potential employers. |
| SimplyHired | – Aggregates job listings from various sources. – Search for data science jobs with specific filters. – Provides salary estimates and company reviews. |
| Skill | Description |
| Analytical Skills | You should have the ability to dissect complex datasets, recognise patterns, and extract actionable insights. This will involve employing critical thinking and logical reasoning to solve problems. |
| Programming Skills | You should have proficiency in programming languages such as Python, R, SQL, and familiarity with tools like Hadoop. You should be capable of writing efficient code for data manipulation, analysis, and modelling. |
| Statistical Skills | You should have an understanding of statistical concepts and methods for data analysis, hypothesis testing, and modelling. This will include knowledge of probability distributions, regression analysis, and experimental design. |
| Machine Learning | You should have knowledge of ML algorithms and techniques for predictive modelling and pattern recognition. This will encompass supervised and unsupervised learning methods, ensemble techniques, and deep learning architectures. |
| Data Visualization | You should have the ability to create compelling visual representations of data using tools like Tableau and Matplotlib. Also, you should be aware about effective data visualisation and facilitate the interpretation of complex information. |
| Domain Knowledge | You should have an understanding of the industry or domain in which data science is applied, facilitating meaningful analysis. You should also possess domain-specific knowledge to contextualise your findings and derive actionable insights. |
| Communication Skills | You should have effective communication skills of findings and insights to both technical and non-technical stakeholders. This will involve articulating complex concepts in a clear and concise manner. |
| Problem-Solving | You should have the capability to identify and solve complex problems using data-driven approaches and creative thinking. You should also learn about designing experiments, and iteratively refining solutions based on feedback. |
| Data Wrangling | You should have skills to preprocess and clean data, handle missing values, and ensure data quality for analysis. This encompasses data transformation, normalisation, and feature engineering techniques to prepare raw data for modelling and analysis. |
As was already said, there is a considerable career scope in the data science field due to the continually growing work requirements. One of the most rewarding and lucrative job categories in the data science industry is data scientist. A data scientist can work for some of the leading multinational corporations and make good money.
Min Salary: ₹3.8 Lakhs per annum
Avg Salary: ₹14 Lakhs per annum
Avg Salary: ₹28 Lakhs per annum
If becoming a data scientist is something you truly want to do, you should know that it requires commitment and hard work in addition to degrees. With the right training and experience, you may get your ideal job and take the lead in the rapidly changing field of technology.
