Career in Data Science
Have you been hearing the term data science quite often these days? Wondering what this means? Let’s dive in together to find out the career in data science and understand why is it so important in today’s world!
Data is information such as facts and numbers used to analyze something or make decisions. Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data Science has been hailed as the “sexiest job of 21st Century” by Harvard Business Review. A data scientist is an individual, organization, or application that performs statistical analysis, data mining, and retrieval processes on a large amount of data to identify trends, figures, and other relevant information.
So where is data science used in real-world applications?
Data is the fuel that drives industries. Big Data has revolutionized companies and has given them an edge in competition. These companies need specialized people who are proficient at handling, managing, analyzing, and understanding trends in data. Industries need data to help them make careful decisions. Data Science churns raw data into meaningful insights. Therefore, industries need data science. The Data Scientist is an expert in various underlying fields of Statistics and Computer Science. A skilled Data Scientist will know how to dig out meaningful information with whatever data they come across. They use their analytical aptitude to solve business problems.
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Companies are using Data to analyze their marketing strategies and create better advertisements. By studying and analyzing customer feedback, companies are able to create better advertisements. The companies do so by carefully analyzing customer behavior online. Also, monitoring customer trends helps the company to get better market insights.
Data Scientists help the company to acquire customers by analyzing their needs. This allows the companies to tailor products best suited for the requirements of their potential customers.
The Data Scientists aid in product innovation by analyzing and creating insights within the conventional designs. They analyze customer reviews and help the companies craft a product that sits perfectly with the reviews and feedback.
Does all this fascinate you and inspire you to become a data scientist but wondering how and where to start? Let’s get started on this beautiful journey to becoming a data scientist!
Career in data science or a Data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data. You can enroll for a master’s degree program in the field of Data Science, Mathematics, Astrophysics, or any other related field. The skills you have learned during your degree program will enable you to easily transition to data science.
Some must Technical skills for making a career in data science:
1) R Programming:- R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve. 2) Python Coding:- Python is a great programming language for data scientists. You can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.
3) Hadoop Platform:- As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. You can use Hadoop for data exploration, data filtration, data sampling, and summarization. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.
4) SQL Database/Coding:- SQL (structured query language) is a programming language that can help you to carry out operations like add, delete, and extract data from a database. It can also help you to carry out analytical functions and transform database structures. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.
5) Apache Spark:- Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time. It also helps data scientists to handle complex unstructured data sets. You can use it on one machine or cluster of machines. Apache spark makes it possible for data scientists to prevent loss of data in data science. The strength of Apache Spark lies in its speed and platform which makes it easy to carry out data science projects.
6) Data Visualization:- Data needs to be translated into a format that will be easy to comprehend. People naturally understand pictures in forms of charts and graphs more than raw data. Data can be visualized with the help of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend.
7) Unstructured data:- Most people referred to unstructured data as ‘dark analytics” because of its complexity. Unstructured data are undefined content that does not fit into database tables. Examples include videos, blog posts, customer reviews, social media posts, video feeds, audio, etc.
Non-Technical Skills for Career in Data Science
1. Intellectual curiosity:- Curiosity is one of the skills you need to succeed as a data scientist. For example, initially, you may not see much insight into the data you have collected. Curiosity will enable you to sift through the data to find answers and more insights. Data scientists spend about 80 percent of their time discovering and preparing data.
2. Business acumen:- To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.
3. Communication skills:- A strong data scientist is someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. As a data scientist, you have to know how to create a storyline around the data to make it easy for anyone to understand. For instance, presenting a table of data is not as effective as sharing the insights from those data in a storytelling format. Using storytelling will help you to properly communicate your findings to your employers.
4. Teamwork:- A data scientist has to work with company executives to develop strategies, work product managers, and designers to create better products, work with marketers to launch better-converting campaigns, work with client and server software developers to create data pipelines and improve workflow. You will literally have to work with everyone in the organization. This needs one to develop remarkable teamwork skills.
Career in Data Science or A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data”. With the Big Data wave showing no signs of slowing down, there’s a rush among global companies to hire Data Scientists to tame their business-critical Big Data. Not only are Data Scientists responsible for business analytics, but they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms. Some of the prominent Data Scientist job titles are:
Data Scientist Data Architect Data Administrator Data Analyst Business Analyst Data/Analytics Manager Business Intelligence Manager
The median starting salary for a data scientist remains high at $95,000. Mid-level data scientist salary. The median salary for a mid-level data scientist is $128,750. If this data scientist is also in a managerial role, the median salary rises to $185,000.
Career in Data Science – Top companies which hire data scientists are:-
Microsoft – 1,623 High-Level Data Employees Facebook – 1,1307 High-level Data Employees IBM – 1,245 High-level Data Employees Amazon – 1,215 High-Level Data Employees Google – 904 High-Level Data Employees Apple – 385 High-Level Data Employees Oracle – 306 High-Level Data Employees
Some books that should be on your bucket list if you want to make a career in data science:- Head First Statistics: A Brain-Friendly Guide-by Dawn Griffiths Practical Statistics for Data by Bruce Hillam Introduction to Probability by Dimitri P. Bertsekas and ·John N. Tsitsiklis Introduction to Machine Learning with Python: A Guide for Data Scientists by Sarah Guido Python Machine Learning By Example by Yuxi (Hayden) Liu Pattern recognition and machine learning by Christopher Bishop Python for data analysis by Wes McKinney Naked Statistics by Charles Wheelan Data Science and big data analytics by Lillian Pierson R for data science by Dan Toomey
If you feel you want some expert guidance and also want to be certified which will add to you CV, Top certification courses to make a career in data science:
Dell EMC Proven Professional Certification Program-
https://education.dellemc.com/content/emc/enus/home/certification-overview.html
Cost: $200 per Proven Professional certification exam. Expiration: Valid for two years only.
Certified Analytics Professional – https://www.certifiedanalytics.org/
Cost: It costs $495 for INFORMS members, $695 for non-members. And the team pricing for organizations is available on request. Expiration: Valid for only three years.
SAS Academy for Data Science
https://www.sas.com/en_us/training/academy-data-science.html
Cost: $2,250 is the minimum price or individual certification courses. Expiration: In this case, credentials do not expire. But some exams may be retired as software changes.
Microsoft Certified Solutions Expert (MCSE)
https://www.microsoft.com/en-us/learning/mcse-certification.aspx
Cost: $125 per exam, per attempt. Expiration: Valid for only three years.
Cloudera Certified Associate (CCA)
https://www.cloudera.com/about/training/certification/cca-dataanalyst.html
Cost: $295 per exam specialty and per attempt. Expiration: Valid for two years only.
Cloudera Certified Professional: CCP Data Engineer
https://www.cloudera.com/about/training/certification/ccp-dataengineer.html
Cost: $600 per attempt — each attempt includes three exams. Expiration: Valid for only three years.
Data Science Certificate – Harvard Extension School
https://www.extension.harvard.edu/academics/professionalgraduate-certificates/data-science-certificate
Cost: It cost $2,700 per course, with a minimum of three to five courses. Expiration: Certificate degree will not expire.
Amazon AWS Big Data Certification
https://aws.amazon.com/certification/certified-big-data-specialty/
Cost: It costs $300 for each attempt Expiration: 2 Years
Oracle Certified Business Intelligence
https://education.oracle.com/learn/business-intelligence/pPillar_47
Cost: Each attempt would cost $245 Expiration: There is no expiration for this certification.
Youtube has become a great platform to learn and will help you to build a career in data science. It is a great medium of online learning for free. Some popular youtube channels to watch out for if you are dreaming of becoming a data scientist are:-
Data Science Dojo DataCamp Sentdex | Learn about Python Programming and Machine Learning Data School 365 Data Science Data Science by Arpan Gupta IIT, Roorkee
So budding data scientist, are you ready to take off?
Feel free to connect with us for free guidance, if you are planning to make a career in data science!
