Data is a dictionary that describes any advancements in the world. Whether it be technology, business, science, let it be any field, they all require a mass of raw data and its clear interpretation to have any development especially in the world of digital technology.
Data Science is the concept of relating raw data to a common factor and analyzing it in a grouped category. The significance of data analysis comes when we need organized information while creating any new system or updating the existing system. Let us answer some basic questions before getting deeper into data science.
Is data science the same as data analytics?
The essential thing to know before stepping into the world of big data is that data analytics is not the same as data science.
A data analyst tends to identify trends and finds a way to organize already perfected data. They represent the unordered collection of information in an organized and accessible pattern, sometimes through visual representation.
A data scientist is a person who interprets data to formulate a material through advanced mathematical and coding techniques.
In simple words, data science creates a domain for possible questions and data analytics discovers answers to the questions being asked.
What do you do in data science?
The job of a data scientist is specific to the organization in which he/she works. If you are working for an insurance company, you may have to collect and analyze data on claims availability, personalized policies, the background of the client, competition with other companies, and so on. But the major responsibilities are common for every organization. Some specific tasks include:
- Identifying the data-analytics problems that offer the greatest opportunities to the particular organization
- Determining the accurate data sets and variables
- Collecting large sets of structured and unstructured data from legit sources
- Cleaning and validating the data to ensure accuracy, completeness, and uniformity
- Devising and applying models and algorithms to mine the stores of big data
- Analyzing the data to identify patterns and trends
- Interpreting the data to discover solutions and opportunities
- Communicating findings to stakeholders using visualization and other means
With this basic understanding, we can now explore the basic steps in a data science learning process.
Steps in data science learning
1. Familiarize with Python
Python and R are the two common languages used in data science. But R is more popular in the academic field and Python is frequently used in the industrial field. For students, Python will be a better option, to begin with, as it is easy to learn and has many in-built functions. Also, many companies are providing internships and projects mostly based on Python. You have to make yourself occupied with one single programming language for learning and gain expertise in it.
2. Data analysis, manipulation, and visualization with pandas
To perform data analysis with Python, you need to be familiar with Pandas Libraries. Pandas provide a high-performance data structure suitable for tabular data with columns like Excel sheet or SQL. The Pandas library provides you with provisions for reading and writing data, handling missing data, filtering data, cleaning messy data, merging datasets, visualizing data, and much more. Having a clear understanding of Pandas is very much important in the path of data science learning.
Some simple data science projects using pandas are given below:
100 pandas puzzles is a project with 100 exercises that test knowledge of pandas’ functionality. The project contains two Jupyter Notebooks, one with just the puzzles and the other with both puzzles and solutions.
data-science-ipython-notebooks is a collection of Jupyter Notebooks including a folder with pandas code examples.
ved-explore is a set of open-source Jupyter Notebooks that demonstrate analysis of the Vehicle Energy Dataset (VED).
3. Machine learning
Building machine learning models is a great way to apply data science tools. However, it is not a necessity to be an expert in ML to perform data science functions but it will be a greater cutting edge through the learning process. Machine Learning proposes smart alternatives to analyze vast volumes of data. It is a leap forward from computer science, statistics, and other emerging applications in the industry. Machine learning can produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of the input data. This will improve the efficiency and speed of your work.
Some machine learning projects with source code are:
Build a python application that will transform an image into its cartoon using machine learning libraries.
Source Code: Image Cartoonifier Project
Create your emoji with Python
The objective of this machine learning project is to classify human facial expressions and map them to emojis. You should build a convolution neural network to recognize facial emotions and then map those emotions with the corresponding emojis or avatars.
Source Code: Emojify Project
The idea behind this project is to build a model that will classify how much loan the user can take. It is based on the user’s marital status, education, number of dependents, and employments.
Dataset: Loan Prediction Dataset
MNIST Digit Classification
The MNIST digit classification python project enables machines to recognize handwritten digits. This project could be very useful for computer vision. Here we use MNIST datasets to train the model using Convolutional Neural Networks.
Dataset: MNIST Digit Recognition Dataset
Source Code: Handwritten Digit Recognition Project
4. Practice your knowledge
The best way to practice your knowledge of data science is to do independent projects and learning to work professionally. Any project experience will be considered a value point when you apply for internships or group projects.
Here are some tips to begin with your first data science project
- Create a workflow:
A workflow means the entire drafting of a project from deciding the problem statement to creating the working model and extending its possibilities to future use.
Here is an example of a workflow chart
The workflow chart helps you analyze the paths easily and if you are working as a team, all team members can easily identify the procedure of work.
- Collect the data
After finding a path to solve a particular problem, you must collect the data needed to arrive at that solution. The data must be legit and clear.
- Data analysis and model evaluation
You must then analyze the data collected and remove unnecessary features. Evaluate the final model then and use them to predict future data. Try improving your model performance through continuous diagnosis and evaluation.
Opportunities for students
There is a wide range of scope for data science enthusiasts from data science scholarships to project companionship and internships. It is important to know the important dates and eligibility for these opportunities. Having hands-on experience will give you an extra edge while moving to a career path in technology.
To learn about data science opportunities available for students, refer to our article on the same.
The world of technology is expanding vividly and at an alarming rate. It is important to be aware of what is going on in our favorite field and train ourselves to be a part of it. A right beginning and correct methodology are the most important things in achieving any goal. So, understand where to begin and how to proceed after doing a good research on whichever field you are interested in.