Data Science has become a buzzword in the 21st century. But what is Data Science? In this tutorial, you can understand what is data science, jobs, tools, applications, jobs, etc.
So let’s start our data science tutorial blog post,
Prerequisite for Data Science
Non-Technical Prerequisite:
- Decision Making: To learn data science, one must have decision-making ability. When you have this ability, you can make wise decisions in critical scenarios.
- Critical Thinking: If you are a person who finds multiple new ways to solve the problem with efficiency, then this is your cup of tea!
- Communication skills: Communication skills ( Reading+Writing+Understanding )are most important for a data scientist for solving a business problem.
Technical Prerequisite:
- Mathematics: A Data scientist is a person who plays with numbers and data! So, mathematical calculations is a much-needed skill for a data scientist.
- Statistics: Basic understanding of statistics ( mean, median, or standard deviation ) is required to extract knowledge and obtain better results from the data.
- Computer programming: At least one programming language like R, Python, Java is required for becoming a data scientist.
- Algorithms: To understand data science, one needs to understand the concept of algorithms. This helps in solving different problems.
- Databases: Understanding Databases such as SQL is a must.
What is Data Science?
- Data Science is an interdisciplinary field!
- It is a combination of different fields such as Data Manipulation, Data Visualization, Statistical Analysis, and Machine Learning
Now, let’s go ahead with who is a data scientist!?
Who is a Data Scientist?
Look at the image above, a Data Scientist is the master of all trades! He should be proficient in maths- statistics, probability, he should be acing the Visualization, and should have great Computer programming skills as well.
Scared? Don’t be.
In a corporate environment, work is distributed among teams and they have their own expertise in the field. Keep in mind! One should be proficient in at least one of these areas to excel in this field.
But Believe me, investing time in data science is worth a million dollars!
Why? Well, let’s look at career opportunities in data science.
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Jobs in data science
The advent of new technology is directly proportional to the rise in various job roles in data science.
Some of the job roles are listed below:
- Data Scientist
- Data Engineer
- Machine learning Engineer
- Data Analyst
- Statistician
- Data Architect
- Data Admin
- Business Analyst
- Data/Analytics Manager
Below is an explanation of some critical job titles in data science.
Data Scientist:
Role: A Data Scientist is a professional who is good at handling data using various tools, techniques, methodologies, algorithms, etc.
Languages Required: R, SAS, Python, SQL, Matlab, Spark.
Data Engineer:
Role: The role of a data engineer is handling large amounts of data and he is responsible for developing, constructing, testing, and maintaining the architecture of large-scale databases.
Languages Required: SQL, R, SAS, Matlab, Python, and Java.
Machine learning Engineer:
Role: The machine learning Engineer is the one who should have a stand on machine learning algorithms such as regression, clustering, classification, decision tree, random forest, etc.
Language Required: Python, C++, R, Java, and Hadoop.
Data Analyst:
Role: Have you heard of the term “ Data Mining” this is exactly what data analysts do! They look for relationships, patterns, trends in data.
Languages Required : R, Python, HTML, JS, C, C+ + , SQL
Did you know?
Tools for Data Science
Following are some tools required for data science:
| Method | Tools |
| Data Analysis | R,Python,Statistics,SAS,Jupyter,RStudio,MatLab,Excel,RapidMinter. |
| Data warehousing | ETL,SQL,Hadoop,Informatica/Talend,AWS Redshift. |
| Data Visualization | R,Jupyter,Tableau,Cognos. |
| Machine Learning | Spark, Mahout, Azure, ML studio. |
Data Science Lifecycle
The life-cycle of Data Science is explained in the diagram below.
1. Discover: Discover the requirements of the project such as the number of people, technology, time, data, and end goal.
2. Data preparation: Following are the tasks at the data preparation level.
- Data cleaning
- Data Reduction
- Data integration
- Data transformation
3. Model Planning: Determine the various methods and techniques to establish the relation between input variables.
Common tools used for model planning are:
- SQL Analysis Services
- R
- SAS
- Python
4. Model Building: Creating datasets for training and testing purposes and applying different techniques such as association, classification, and clustering, to build the model.
Common Model building tools:
- SAS Enterprise Miner
- WEKA
- SPCS Modeler
- MATLAB
5. Operationalize: In this phase, providing technical documents will help in getting an overview of project performance before the full deployment.
6. Communicate results: In this phase, we will communicate the findings and final results with the business team.
Applications of Data Science
- Internet Search :
- In Google, You get what you wish for! Isn’t it? That’s Data Science.
- Recommendation Systems
- Do you often get a friend’s suggestion list on Facebook? That’s the Data Science behind the recommendation system.
- Image & Speech Recognition
- Speech recognition systems – Ex: Siri, Google assistant, runs on the technique of Data Science.
- Image Recognition – Ex: Facebook recognizes your friend when you upload a photo with them, with the help of Data Science.
Wrapping Up.
Data Science is a vast subject, a combination of several technologies and disciplines. This field best fits those who have a knack for experimentation and problem-solving.
