What is Data Science?
Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.
It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful.
Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.
In short, we can say that data science is all about:
- Asking the correct questions and analyzing the raw data.
- Modeling the data using various complex and efficient algorithms.
- Visualizing the data to get a better perspective.
- Understanding the data to make better decisions and finding the final result.
Need for Data Science:
Some years ago, data was less and mostly available in a structured form, which could be easily stored in excel sheets, and processed using BI tools.
But in today's world, data is becoming so vast, i.e., approximately 2.5 quintals bytes of data is generating on every day, which led to data explosion. It is estimated as per researches, that by 2020, 1.7 MB of data will be created at every single second, by a single person on earth. Every Company requires data to work, grow, and improve their businesses.
Now, handling of such huge amount of data is a challenging task for every organization. So to handle, process, and analysis of this, we required some complex, powerful, and efficient algorithms and technology, and that technology came into existence as data Science. Following are some main reasons for using data science technology:
- With the help of data science technology, we can convert the massive amount of raw and unstructured data into meaningful insights.
- Data science technology is opting by various companies, whether it is a big brand or a startup. Google, Amazon, Netflix, etc, which handle the huge amount of data, are using data science algorithms for better customer experience.
- Data science is working for automating transportation such as creating a self-driving car, which is the future of transportation.
- Data science can help in different predictions such as various survey, elections, flight ticket confirmation, etc.
Types of Data Science Job
If you learn data science, then you get the opportunity to find the various exciting job roles in this domain. The main job roles are given below:
- Data Scientist
- Data Analyst
- Machine learning expert
- Data engineer
- Data Architect
- Data Administrator
- Business Analyst
- Business Intelligence Manager
Prerequisite for Data Science
Non-Technical Prerequisite:
- Curiosity: To learn data science, one must have curiosities. When you have curiosity and ask various questions, then you can understand the business problem easily.
- Critical Thinking: It is also required for a data scientist so that you can find multiple new ways to solve the problem with efficiency.
- Communication skills: Communication skills are most important for a data scientist because after solving a business problem, you need to communicate it with the team.
Technical Prerequisite:
- Machine learning: To understand data science, one needs to understand the concept of machine learning. Data science uses machine learning algorithms to solve various problems.
- Mathematical modeling: Mathematical modeling is required to make fast mathematical calculations and predictions from the available data.
- Statistics: Basic understanding of statistics is required, such as mean, median, or standard deviation. It is needed to extract knowledge and obtain better results from the data.
- Computer programming: For data science, knowledge of at least one programming language is required. R, Python, Spark are some required computer programming languages for data science.
- Databases: The depth understanding of Databases such as SQL, is essential for data science to get the data and to work with data.
Tools for Data Science
Following are some tools required for data science:
- Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner.
- Data Warehousing: ETL, SQL, Hadoop, Informatica/Talend, AWS Redshift
- Data Visualization tools: R, Jupyter, Tableau, Cognos.
- Machine learning tools: Spark, Mahout, Azure ML studio
👍