“Businesses are waking up to the wonders that they can achieve with Data Science, Machine Learning, and Artificial Intelligence.”
Data science is the discipline of making data useful and it’s moving towards a new space where machines can be taught to learn from data to find conclusive insights. It involves a variety of processes, scientific tools, algorithms, and knowledge extraction systems from structured and unstructured data to find meaningful patterns and insights in it.
Trends In Data Science
A few of the top Data science trends are explained below:
- Graph Analytics- It solves the problems by acting as a powerful and flexible tool that analyzes complex data points and relationships using graphs. It represents the complicated data abstractly and in a visual form that is easier and offers maximum and meaningful insights.
- Data Fabric- It is a new trend nowadays in data science. At its core, data fabric encapsulates a company’s data collected from a vast number of sources such as APIs, semantic tires, reusable data services, pipelines, providing transformable access to data.
- Data Privacy by Design- This trend incorporates a safer and proactive approach to collect and handle user data. Corporations need user data to train their models on real-world scenarios.
- Augmented Analytics- It refers to driving better insights from the available data by excluding any incorrect conclusion. By infusing Machine learning and artificial intelligence, this analytics aids users in creating a new model.
- Python- Python is considered as a De-Facto language for data science and is an all-rounder programming language. It is an entry point to getting into the world of data science and artificial intelligence.
Well, the world of data science looks quite promising. No doubt, the demand for data scientist training has shot up. This article will introduce you to the Data Scientist role and its crucial day-to-day responsibilities.
What is a Data Scientist?
Data Scientists are the professionals who gather and analyze large sets of structured and unstructured data. They are big data wranglers who combine skills such as statistics, computer science, programming, and mathematics. Their main job is to analyze, process, and model data then find the results to create actionable plans for companies and organizations. Data Scientists make sense of messy, unstructured data from different sources like social media, smart devices, and emails that don’t neatly fit into a database.
Data Scientists are analytical experts who implement their skills in both social science and technology to find patterns, trends and manage data. They use skepticism of existing assumptions, contextual understanding, and industry knowledge to uncover the best solutions to business issues/challenges. They are also tasked with developing a company’s practices like cleaning, processing, and storing data. As they straddle both the business and IT worlds, they are well-paid and highly sought-after.
A Data Scientist does all this work through effective communication, business domain expertise, and result interpretation, and utilization of relevant statistical techniques, software packages and libraries, programming languages, and data infrastructures.
Data Scientist’s goals and deliverables- Main goals and deliverables of a data scientist are: Prediction (predict a value based on inputs), Classification (spam or not spam), Recommendations (e.g., Amazon and Netflix recommendations), Patterns detection and grouping (e.g., classification without known classes), Anomaly detection (fraud detection), Recognition ( Text, image, audio, video), Actionable insights (via dashboards, reports, visualization), Automated processes and decision-making (credit card approval), Scoring and Ranking(FICO score), Segmentation( demographic-based marketing), Optimization (Risk Management), and forecast (sales and revenue).
Data scientists involve the following terms and technologies in their toolbox.
Data Visualization- Data scientists present data in a particular format so it can be easily analyzed.
Machine Learning- It is a branch of artificial intelligence that is based on automation and mathematics algorithms.
Deep Learning- It is a part of machine learning research that uses data to model complicated abstractions.
Pattern Recognition- This technology recognizes patterns in data that is used interchangeably with machine learning.
Data Preparation- This is used to convert raw data into another format so it can be consumed more easily.
Text Analytics- This technology is specially used to examine unstructured data to glean key business insights.
Day To Day Responsibilities Of A Data Scientist
Every data science job has an air of mystery to its day to day roles and responsibilities. There are several factors that influence the typical day for a data scientist. Here are some of the crucial responsibilities explained below.
- Defining The Data Science Problem- It is the primary step that every data scientist follows along with the data science pipeline is to identify and define the business problem. This process involves various tasks such as detecting and understanding the business issues/requirements, scoping an efficient solution and planning the final data analysis.
- Gather Raw Data- After finding the problem, a data scientist’s second step is to gather raw data for analysis of the defined problem. In this process, their essential task is to find the data sources they have to dig in to get all the relevant data, choose the field, and collect information in one place. Further data scientists clean and organize the collected raw data to sort out errors, remove the missing value, and find duplicate records.
- Decide on an Approach to Solve the Data Science Problem- A data scientist’s next step is to explore the best and the most efficient methods to answer the asked questions. Some of the answers are given through K- means clustering algorithm, which is required for a large dataset. Questions could also be answered with a simpler distance calculation method. Data scientists are applying various algorithmic approaches such as the Two-class Classification Approach, Multi-Class ClassificationApproach, Reinforcement Learning Algorithm, Regression, Clustering, etc.
- Perform In-Depth Data Analysis- Data scientists are responsible for building automated machine learning pipelines and personalized data products to get valuable information for accurate decision making. They use open-source data science tools and libraries in Python and R to get high-value insights.
- Communicate Insights to the Stakeholders- The next most important role of a data scientist is to effectively communicate and convey the findings to stakeholders so that they easily understand the insights and take further better steps and actions. For these important processes, data scientists use data visualization techniques such as QlikView, Tableau, ggplot, Matplotlib to demonstrate real-life cases.
So this is how a typical day looks like in the work of a data scientist. It is a very crucial job with complex responsibilities. If someone wants to land a career in data science start gaining skills with a proper pathway that will help you to become a perfect and successful Data Scientist.