We are living in unprecedented times right now. Never has this generation faced a similar pandemic before. Uncertainty is in the air, and this can be seen in every aspect of our lives. Industries such as travel and tourism, food, and education have taken a pernicious hit. No one is willing to invest in the current financial climate. But this lack of investment might cause you even more harm. It has become essential to spend wisely in the current market. That’s why it has become important to scrutinize every piece of information. That’s where data science comes into the fray. Data science after COVID has become essential for every industry, and a comprehensive and updated Data Science Course covering contemporary aspects of data science is crucial here.
Through data science, a company can maintain the levels of productivity even amid an unsettled environment. Through this article, you will learn the benefits of adopting data science after COVID. But first, we will have to examine the usefulness and effectiveness of the data science Course in empowering businesses and professionals curious about the data-market after COVID.
Data science during COVID
Table of Contents
- Data science during COVID
- Data Science after COVID
- Role of Data Science Course after COVID
Data science is a process in which trained data scientists break down data by training machine learning models. Machine learning is achieved by running the data over and over again in machine learning models. This data is then understood and implemented by the data scientist to get the best results.
The problem that the current crop of data scientists face is the problem of uncertainty.
As the real-time data is dynamic, the pandemic has brought about a change in how data is processed.
Data scientists incorporate techniques to ascertain the future, but at this moment, the future has become uncertain, which has led the data scientists to change the way they look at data.
Adverse effects of COVID on data science
1) Increased competition
As work from home has become the new normal in the current climate. Companies are not bound to hire employees from a specific geographical region.
This change in data science after COVID and the hiring strategy has created competition for data scientists worldwide. Individuals are willing to work longer hours at a lower salary, which has allowed companies to cut costs by hiring such individuals.
Now data scientists not only have to compete with individuals in their vicinity, but they also have to compete with the best data scientists all over the world.
2) Difficulties in learning while sitting at home
Developing your current skills and updating your skills are crucial to make headway in any industry. As the skill set becomes better, it becomes more monetizable.
Hence, it is essential for anyone in the workforce to constantly keep working on their skillset. The skillset can only be improved in an environment that facilitates growth, where the employee is pushed every day. But in the current climate, it is impossible to create such an environment.
Employees have to work from home; they cannot attend training programs and in-person workshops to sharpen their skills. COVID has taken away the opportunity to learn new skills effectively.
3) Cooperating with other team members becomes hard
Cooperation is vital for the functioning of any business and organization.
Without cooperation, it is impossible to achieve goals. Due to the COVID pandemic, employees have to communicate through ineffective channels. It has become exceedingly difficult to get one’s point across the board.
Not just that, anyone who has a question about the project finds it hard to communicate with the right information source. Many employees find it hard to understand directives unless they’re explained what is to be done in person.
Data Science after COVID
It is important here to create new data sets. These data sets should keep in mind various changes that society is going through during this pandemic. Concepts such as social distancing and lockdown have become an essential part of human behaviour.
That is why it is necessary to create new data sets keeping these new concepts in mind. We need new solutions to fight this unprecedented situation; implementing old techniques won’t garner satisfactory results.
At this moment in time, a lot of data is being collected by companies. But some companies fail to mine and utilize this data. Data scientists have become a necessary commodity for every company that wants to process data.
Pursuing a data science course will provide you with all the tools required to process complex data.
Role of Data Science Course after COVID
We have to come to terms with this new reality. Social distancing and lockdowns have become a norm post COVID.
Data scientists need to look at data from a different perspective now. Data scientists need to analyze new data and, based on it, devise the best strategy to help their employers.
The consumer’s change in behaviour needs to be understood; having this information will enable you to take the right approach. Aspiring data scientists should also take into account the possibility of another wave of COVID.
The second wave will bring new challenges that can only be surmounted by processing the data collected during the first wave and repeatedly changing the data set required to make predictions and assessments.
Updated Data Science Course after COVID should be able to bring all such new challenges to the fore.
The well-updated & comprehensive Data Science Course includes-
- Introduction to Data Science & Analytics Techniques
- Python Fundamentals
- Python MySQL
- Pandas DataFrame & Data Analysis
- Statistics Fundamentals
- Machine Learning with Python
- Regression & Classification
- Support Vector Machine Introduction
- Machine Learning – Clustering Introduction
- Recommender Systems
- Introduction to NLP
Role of Data Scientists
Data science after COVID will present a different picture. The demand for data scientists hasn’t decreased during the pandemic, and there’s no reason whatsoever that it will change after it’s over.
Companies are constantly collecting data, and they need someone to make sense of all this collected data. This collected data is of no use to the company unless it is simplified enough to be implemented by a company. The need for smarter business decisions has never been so desperate and deeply dependent on insightful decision making.
The industry is constantly searching for thought leaders who have the right skill sets to perform the given task at hand. The new generation of data scientists should not only be comfortable mining data, but they should also be skilled enough to operate database management solutions.
Visionary analysts are required that are capable of scoping and understanding problems. After understanding the problem, they should be capable of devising a cost-effective plan and giving the best results.
Key concepts a data scientist needs to be aware of-
- Data science and python
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data, Raw and Processed Data and Data Wrangling
- Overview of the Analytics Techniques
- Analytics, Business Analytics, Business Intelligence
- Environment Setup, Database Connection, Creating a New Database
- Creating Tables, Insert Operation, Read Operation, Update Operation, Join Operation
- Performing Transactions, array, Array Creation, Data Type Objects
- Data type Object (dtype) in NumPy, Indexing, Basic Slicing, and ALinear Algebra
- Sorting, Searching, and Counting
- Set 1 (Introduction)advanced Indexing
- Iterating Over Array, Binary Operations, Mathematical Function, String Operations
- Creating a Pandas DataFrame, Dealing with Rows and Columns in Pandas DataFrame
- Indexing and Selecting Data with Pandas, Boolean Indexing in Pandas
- Conversion Functions in Pandas DataFrame
- Iterating over rows and columns in Pandas DataFrame
- Graphically Displaying Single Variable
- Measures of Location, Measures of Spread, Displaying relationship – Bivariate Data
- Scatterplot, Measures of association of two or more variables
- Covariance and Correlation, Probability, Joint Probability, and independent events
- Conditional probability, Bayes’ Theorem, Prior, Likelihood and Posterior
- Discrete Random Variable, Probability Distribution of Discrete Random Variable
- Applications of Machine Learning, Supervised vs Unsupervised Learning
- Python libraries are suitable for Machine Learning, NLP, etc.
Opportunities for Data Scientists in the current market
COVID has been the reason for the melancholy of many individuals. But one should look at the silver lining in situations that they can’t control.
Offices were shut down to curtail the spread of COVID. Employees were asked to work from home. Due to this new culture, data scientists can take advantage of new opportunities in different locations without leaving their humble abode.
Data science after COVID allows aspiring data scientists to get high paying jobs all over the country and outside of the country if they have the right qualifications and work ethic.
An aspiring data scientist can take up a data science course to get a certification in data science. This data science course will provide them with the right tools to apply for a job in an industry that’s booming; an industry that has not been affected even after COVID.
Even though the pandemic has hit the job market hard, the sphere of data science is still unaffected. There are a lot of new opportunities every day in the market.
That being said, the new age of data scientists faces many competition and other difficulties that arise because of working from home.
An aspiring data scientist can pursue a data science course to learn the skill set required to get a job in this esteemed field. Data science after COVID will never be the same. Enrolling in a Data Science Course can help you be a pro-level data scientist.