Data Science Tutorial for Beginners
Data Science has become the most demanding job of the 21st century. Every organization is looking for candidates with knowledge of data science. In this tutorial, we are giving an introduction to data science, with data science Job roles, tools for data science, components of data science, application, etc.
So let's start,
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.
Example:
Let suppose we want to travel from station A to station B by car. Now, we need to take some decisions such as which route will be the best route to reach faster at the location, in which route there will be no traffic jam, and which will be cost-effective. All these decision factors will act as input data, and we will get an appropriate answer from these decisions, so this analysis of data is called the data analysis, which is a part of data science.
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.
Data science Jobs:
As per various surveys, data scientist job is becoming the most demanding Job of the 21st century due to increasing demands for data science. Some people also called it "the hottest job title of the 21st century". Data scientists are the experts who can use various statistical tools and machine learning algorithms to understand and analyze the data.
The average salary range for data scientist will be approximately $95,000 to $ 165,000 per annum, and as per different researches, about 11.5 millions of job will be created by the year 2026.
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
Below is the explanation of some critical job titles of data science.
1. Data Analyst:
Data analyst is an individual, who performs mining of huge amount of data, models the data, looks for patterns, relationship, trends, and so on. At the end of the day, he comes up with visualization and reporting for analyzing the data for decision making and problem-solving process.
Skill required: For becoming a data analyst, you must get a good background in mathematics, business intelligence, data mining, and basic knowledge of statistics. You should also be familiar with some computer languages and tools such as MATLAB, Python, SQL, Hive, Pig, Excel, SAS, R, JS, Spark, etc.
2. Machine Learning Expert:
The machine learning expert is the one who works with various machine learning algorithms used in data science such as regression, clustering, classification, decision tree, random forest, etc.
Skill Required: Computer programming languages such as Python, C++, R, Java, and Hadoop. You should also have an understanding of various algorithms, problem-solving analytical skill, probability, and statistics.
3. Data Engineer:
A data engineer works with massive amount of data and responsible for building and maintaining the data architecture of a data science project. Data engineer also works for the creation of data set processes used in modeling, mining, acquisition, and verification.
Skill required: Data engineer must have depth knowledge of SQL, MongoDB, Cassandra, HBase, Apache Spark, Hive, MapReduce, with language knowledge of Python, C/C++, Java, Perl, etc.
4. Data Scientist:
A data scientist is a professional who works with an enormous amount of data to come up with compelling business insights through the deployment of various tools, techniques, methodologies, algorithms, etc.
Skill required: To become a data scientist, one should have technical language skills such as R, SAS, SQL, Python, Hive, Pig, Apache spark, MATLAB. Data scientists must have an understanding of Statistics, Mathematics, visualization, and communication skills.
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.
Difference between BI and Data Science
BI stands for business intelligence, which is also used for data analysis of business information: Below are some differences between BI and Data sciences:
| Criterion | Business intelligence | Data science |
|---|---|---|
| Data Source | Business intelligence deals with structured data, e.g., data warehouse. | Data science deals with structured and unstructured data, e.g., weblogs, feedback, etc. |
| Method | Analytical(historical data) | Scientific(goes deeper to know the reason for the data report) |
| Skills | Statistics and Visualization are the two skills required for business intelligence. | Statistics, Visualization, and Machine learning are the required skills for data science. |
| Focus | Business intelligence focuses on both Past and present data | Data science focuses on past data, present data, and also future predictions. |
Data Science Components:
The main components of Data Science are given below:
1. Statistics: Statistics is one of the most important components of data science. Statistics is a way to collect and analyze the numerical data in a large amount and finding meaningful insights from it.
0 Comments