What Is Data Science? Beginner-Friendly for Freshers
What Is Data Science? Beginner-Friendly Explanation for Freshers
Introduction - Why Data Science is the Most In-Demand Skill for Freshers Today
Data Science has become a buzzword in the IT industry, as there are many companies from a variety of industries (IT, Banking, E-commerce, Healthcare, Education, Transport, Retail, and Entertainment) that have large amounts of Data they collect every day. By far, the most sought-after Data Science professionals are Data Scientists who can extract meaning from Data and give meaningful insights to organisations. The rapid growth in the need for Data Professionals is evident in the number of companies that now want to know: what do Customers Want, why are some Business Processes failing, what will the Trend be in the Future, and how do I make Smarter Decisions? Many Freshers believe that Data Science is an extremely difficult Field and only for Math Geniuses. The truth is that Data Science is simply a combination of Logical Thinking, Analytical Skills and Basic Technical Tools, and you do not have to know everything on Day 1. And the good news for Freshers from non-Technical Backgrounds (B.Com, BBA, BA, Science) have successfully started their careers in Data Science. Data Science as a career path offers a Beginner's Level of Knowledge and an abundance of career opportunities, including Data Analyst, Data Engineer, Business Analyst, ML Engineer and AI Specialist. It is important to understand what is needed in order to enter the world of Data Science.
What Is Data Science? A Simple Explanation Even a Fresher Can Understand
Data Science is the way in which we collect, clean, analyse and interpret data for the purpose of making decisions or solving problems.
The three core disciplines of Data Science are:
- Statistics & Mathematics - to provide an understanding of patterns and insight
- Programming (Python /SQL)- for the manipulation and maintenance of datasets
- Domain Knowledge - to have an understanding of the business you are working for.
When these three areas come together, a Data Scientist will have the tools to turn large, complex sets of data into actionable insights.
- Netflix uses Data Science to create movie recommendations.
- Amazon uses Data Science to create product recommendations.
- Banks use Data Science to identify fraud.
Data Science uses coding models to help us solve real-world problems and use data effectively. The Data Science Process is as follows:
- Problem Definition
- Data Collection
- Data Cleaning/Preparation
- Data Exploration, Utilization & Visualization
- Algorithms/Statistical Methods to Analyse Data
- Insights Generation
- Results Presentation and Data Dashboard Creation
Beginning Data Scientists should start off using Microsoft Excel, SQL, and minimal programming using Python. There are many layers of complexity and insight into Data Science, and as Data Scientists progress, they will get into Areas of Study, such as Machine Learning and AI. Data Science is unique because it combines both Creativity & Logic.
Welcome to Data Science: Key Tools, Skills & Technologies (Roadmap for Beginners)
The reality is, you can learn Data Science step/based.
Database Skills (SQL)
Almost all companies use SQL to hold data in their databases. SQL enables you to pull, filter, and arrange your data easily. For someone new to the Data Science field, SQL is often the first skill/technical skill that hiring managers are looking for.
Excel & Spreadsheet
Excel helps you learn about formulas (calculations), images or visual presentations of your data (charts), trends that appear in your data, and how to arrange (structure) your data. Because most entry-level Data Analyst jobs heavily use Excel, this is an important skill to have if you want to get an entry-level Data Analyst job.
Data Visualisation Tools (Power BI, Tableau)
Both Power BI and Tableau create great-looking visual dashboards/designs that companies use for their business decisions. Those two tools can be learned very quickly by anyone with some technical skills. They are very impressive and attractive to potential hiring managers, since you do not have to have any programming experience.
Statistics & Mathematics
To become a Data Analyst, you do not need to have a PhD in Mathematics. However, you will want to have a basic understanding of the basic statistical concepts (mean, correlation, probability, etc.) as well as distributions, linear regression, and hypothesis testing. All of these concepts mentioned are enough to provide someone new with a great foundation in statistics and mathematics.
- Machine Learning (Optional for Freshers)
- Data Preparation and Correction
- Most Data Science tasks (70-80%) involve processing unorganised data into organised data.
An example of how an entry-level Data Scientist’s career path may unfold would be as follows: Use of Excel → Learn SQL → Start using Python → Learn Statistics → Learn how to use Data Visualisation Tools → Create Mini Projects that showcase skills and expertise → Create Portfolio to display skills and expertise → Obtain Internships. By using this sequential format, you should gain a greater understanding of Data Science and improve your ability to convey your findings..
Applications of Data Science That a Freshman Can Apply to Their Life.
From hospitals predicting which diseases you may develop and predicting what symptoms a patient may experience, as well as improving the way we treat patients by eliminating any mistakes through Medical Error Prevention, to Machine Learning Models to Pre-Detect & Predict the occurrence of Cancer, to Long-Term Predictions on Heart Attacks. Fraud Detection: Banks utilise Data Science to identify suspicious ATM cash withdrawals by segmenting purchases & ATM usage patterns to approve/decline loans using risk assessment analysis to segment customers into credit risk categories/standards for determining which loans should be approved/declined, etc.
Social Media Apps/Historical Trends promote posts based on how you interacted with them in the past. For example, Instagram uses machine-learning algorithms to show you posts you have previously liked, and YouTube recommends videos similar to those of your previous viewing history, and Netflix makes recommendations for movies that are being watched the most frequently based on your personal preferences & historical trends. All of these examples utilise Data Science to produce their results based on patterns of previous behaviours.
Transportation and travel
Google Maps sends alerts on traffic patterns. Uber computes the costs of rides based on market efficiency and matches drivers accordingly. Airlines utilise predictive models to price tickets based on how many are available for sale.
Manufacturing and automation
AI technologies have improved preventative maintenance through the use of machine learning algorithms, allowing for the prediction of machine failures prior to occurring; thus, increasing the efficiency and reducing both time and costs associated with maintaining machines.
Education and edtech
Education technology platforms provide a way for teachers to assess their students’ performance and offer tailored learning solutions based on those assessments.
Career Opportunities, Salaries & How Freshers Can Start Their Data Science Journey
The beauty of data science is that it can be applied to almost any industry. Data science has many opportunities available right now for entry-level candidates because companies in every industry are now seeking out data scientists.
- Business Analyst – responsible for matching business needs with analytics derived from data
- Data Scientist – responsible for developing algorithms and predictive analytics to address complex issues
- Machine Learning Engineer – responsible for designing machine learning algorithms and systems
- Data Engineer – responsible for monitoring the data flow through databases and managing ETL processes
- Business Intelligence Analyst – responsible for creating dashboards and BI solutions to provide a visual overview of data
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