Among the most popular and in-demand career paths for skilled professionals, data science continues to grow and develop. The best data professionals in the world today understand that data analysis, data mining, and programming skills are no longer enough. Data scientists must study the entire spectrum of the data science life cycle, as well as have flexibility and an understanding of how to maximize returns at every phase of the process in order to uncover useful intelligence for their organizations. The study of data is known as data science. Analyzing, visualizing, managing, storing, and managing data to generate insights is what it involves. Data-driven decisions can be made using these insights. Using both structured and unstructured data is necessary for Data Science applications. Statistics, mathematics, and computer science are at the foundation of this multidisciplinary field. Having an abundance of data science jobs and a lucrative pay-scale make it one of the most sought-after jobs.
Additional Info
Why is Data Science Important?
Magic happens when data is used. Data can assist industries in making careful decisions. Raw data is turned into meaningful insight by data science. Thus, industries require data science.. Data Scientists are wizards who are able to create magic through data.
A skilled Data Scientist will be able to extract valuable information from any data that he is presented with.ss. The company benefits from his assistance. Data-driven decisions are a crucial part of the company, and he is the expert in this area.
An expert in numerous underlying areas of statistics and computer science, a Data Scientist is an expert. The ability to analyze problems helps him solve business problems.
Roles & Responsibilities of a Data Scientist
Business stakeholders work closely with data scientists to understand their goals and determine how data can be used to meet those goals. They design data modeling processes, develop algorithms and predictive models to extract the data the business needs and help analyze and share insights with peers. While each project is different, the process for gathering and analyzing data generally follows the below path:
Management:
The Data Scientist plays a minor managerial role. He assists in constructing the proficiency base of data analytics projects and provides support for numerous ongoing and planned projects.
Analytics:
The Data Scientist performs a scientific role, planning, implementing, and assessing statistical models and strategies for both internal and external business applications. An economist or statistician develops models for various problems with econometrics and statistics such as projection, classification, clustering, pattern analysis, sampling, simulations, etc.
Strategy/Design:
In addition to helping businesses understand consumer trends and management, the Data Scientist also helps solve business problems, such as the optimization of product fulfillment and entire profits.
Collaboration:
In this position, a data scientist works towards enhancing business performance and decision-making by collaborating with superior data scientists.
Knowledge:
Data Scientists also take the lead in exploring new technologies and tools with the goal of providing the business with the most insightful information possible through data-driven insights. In this situation, the Data Scientist also uses initiative in assessing and utilizing new and enhanced data science methods for the business, which he delivers to senior management of approval.
Other Duties:
In addition, Data Scientists perform tasks that are assigned to them by a Senior Data Scientist, Head of Data Science, or Chief Data Officer.
Essential Data Science Skills
Most data scientists use the following core skills in their daily work:
Statistical analysis:
Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.
Machine learning:
Implement algorithms and statistical models to enable a computer to automatically learn from data.
Computer science:
Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
Core skills used by most data scientists include:
Statistical analysis:
Patterns can be identified in data. Pattern detection and anomaly detection are among the most important skills.
Machine learning:
Automate the process of learning from data by using algorithms and statistical models.
Computer science:
Use the principles of computer science, artificial intelligence, database design, human-computer interaction, and numerical analysis.
Programming:
Analyzing large datasets and writing computer programs to uncover answers to complex problems. Programming skills including Java, R, Python, and SQL are crucial for data scientists.
Data storytelling:
Use data to communicate actionable insights to a non-technical audience.
An organization's ability to make sound decisions relies on the contributions of data scientists. Thus, their "soft skills" must include the following.
Business intuition:
Get to know your stakeholders so you can fully understand the challenges they would like to overcome.
Analytical thinking:
Analyze abstract business issues to find analytical solutions.
Critical thinking:
Make a decision based on an objective analysis of the facts.
Inquisitiveness:
Discover patterns and solutions within the data by looking beyond what's on the surface.
Interpersonal skills:
A company's communication must reach a diverse audience at every level.
Benefits of data science
Both data and science that decodes it are valuable. Currently, data is worth more than oil, and billions of bytes are being generated every day. Organizations in many different industries will depend heavily on data scientists in the future.
Data without science is nothing.
Reading and analyzing data is necessary. To be able to make data-driven discoveries, it is vital to possess a quality of data and to know how to interpret it.
Data will help to create better customer experiences.
Machine learning will be the enabling technology for the development of products for goods and services that customers will love. For example, an eCommerce company could discover their customer personas by analyzing their purchase history with a recommendation system.
Data will be used across verticals.
Consumer goods, tech, and healthcare are not the only areas where data science is used. From banking and transportation to manufacturing, there will be a high demand for optimizing business processes using data science. A whole new world of opportunities will open up for anyone who wants to become a data scientist. The future belongs to data.
Why do you want to learn data science
5 valuable reasons to pursue data science as a career
Here are the 5 reasons why you must learn data science.
Great career trajectory with data science – Yes, you will have rewarding career growth in this field. In today's scenario, and for the foreseeable future, data scientists bring a lot of value to organizations.
Great potential to branch out with different options – You can become a data scientist, an analyst, or a ML engineer, or you can even become a data scientist manager.
Highest salary takeaway quotient – As a Data scientist, you can expect to take away a great salary package. Due to the critical role and responsibilities of data scientists, they often receive excellent salaries, sometimes even substantially above market rates.
Become a decision-maker – Not every job opportunity will give you the power to make informed business decisions. That is the core responsibility of a data scientist. It's a great way to get your creative juices flowing. There is no talent pool in the ecosystem, so credibility will always be rewarded.
Less competitive because it is a highly analytical role – Competition is less, but demand is not. The talent pool for these positions is very limited, so hiring is always a challenge for businesses. Your unique skill set will make you less competitive with others in your organization when you join in.
Data Scientist Qualifications Companies Look For in a Candidate
Data Scientist roles and responsibilities include identifying business trends and changes through advanced Big Data Analytics and using a variety of techniques to interpret results from multiple data sources through statistical analysis, data aggregation, and data mining. This is a very important role, and most companies expect some or all of the following Data Scientist skills in candidates before hiring them.
Typical responsibilities of a Data Scientist include identifying business trends and changes through advanced Big Data Analytics, data aggregation, and data mining, and interpreting results from multiple data sources. Companies look for these skills in candidates before hiring them, as this is a very important role.
Problem-solving aptitude with a natural inclination
Possess experience with/knowledge of statistical programming languages, including R, Python, SLQ, etc., for analyzing and extracting information from data
Knowledge of and experience with data architectures
The ability to analyze and analyze the pros and cons of various Machine Learning techniques including decision trees, clustering, artificial neural networks, etc.
A working knowledge of and experience with advanced statistical techniques, such as regression, distribution properties, graphical analysis, etc.
Collaboration between teams can be enhanced by good communication skills
New technologies compel you to learn them and master them
Knowledge of several programming languages, including Java, JavaScript, C, C++, etc.
Data mining experience including GLM/regression, social networks analysis, text mining, etc.
Working knowledge of major web services, such as S3, Spark, and Redshift.
An understanding of distributed data and computing tools, including MapReduce, MySQL, Hadoop, Spark, and Hive.
Use of data visualization tools such as D3, ggplot, Periscope or others to showcase data to stakeholders
Job Roles in Data Science
Data Analyst
Data Engineers
Database Administrator
Machine Learning Engineer
Data Scientist
Data Architect
Statistician
Business Analyst
Data and Analytics Manager
1. Data Analyst
An analyst's responsibilities include analyzing huge amounts of data, visualizing and munging it, and processing it. Occasionally, they must also query the databases. Data analysts need to be able to optimize their work. In order to leach information from some of the biggest databases without destroying them, they must devise and modify algorithms.
How to become a Data Analyst?
The most popular technologies for data analysis are SQL, R, SAS, and Python. This makes certification in these areas a great asset when looking for a job. Problem-solving skills are also important.
2. Data Engineers
To enable data scientists to run their algorithms on stable and highly optimized data systems, data engineers build and test scalable Big Data ecosystems for businesses. Additionally, data engineers improve the efficiency of databases by replacing outdated systems with newer, more advanced versions of current technology.
How to become a Data Engineer?
Technology requisites that you should become proficient in include Hive, NoSQL, Ruby, Java, C++, and Matlab if you are interested in a career as a data engineer. Also, you need to be familiar with popular APIs for data and ETL tools.
3. Database Administrator
Database administrators are responsible for the proper functioning of all databases in an organization and grant or revoke their services according to the requirements of employees. Backups and recoveries of databases are also their responsibility.
How to Become a Database Administrator?
Backup and recovery of databases, data security, data modeling and design are some of the competencies essential to a database administrator. A good disaster management skill is a plus.
4. Machine Learning Engineer
Currently, there is a great demand for engineers with machine learning skills. Nevertheless, there are challenges associated with the job profile. Besides having a thorough understanding of SQL, REST APIs, and other powerful technologies, machine learning engineers need to perform A/B testing, construct data pipelines, and implement computational algorithms such as classification and clustering.
How to Become a Machine Learning Engineer?
As a start, you ought to be familiar with some technologies such as Java, Python, and JS. You should also be familiar with mathematics and statistics. It is much easier to crack a job interview once you have mastered both.
5. Data Scientist
By analyzing and processing data, data scientists are able to come up with the most effective solutions to business challenges. To provide actionable insights, they undertake predictive analysis and run a fine-tooth comb through "unstructured/disorganized" data. These companies can also do this by identifying trends and patterns that can help them make better decisions.
How to Become a Data Scientist?
As a data scientist, you must be proficient in R, MatLab, SQL, Python, and other related technologies. It can also help if you have a higher degree in mathematics or computer engineering, etc.
6. Data Architect
Creating data architectures for integrating, centralizing, and safeguarding data is what a data architect does. Additionally, they ensure that the data engineers have access to the best tools and systems.
How to Become a Data Architect?
Data architecture requires knowledge of data warehouses, data modeling, and extraction, transformation, and loan processes (ETL). Besides Hive, Pig, and Spark, you should also have a good understanding of them.
7. Statistician
Statistics suggests that a statistician has a good understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.
How to Become a Statistician?
Statisticians must be passionate about logic. As well as SQL, data mining, and other machine learning technologies, they are also proficient with a variety of database systems.
8. Business Analyst
In comparison with other data science jobs, the role of a business analyst is a bit different. Although they have a good understanding of how data-oriented technologies work and how to handle large amounts of data, they also separate the high-value data from the low-value data. By defining the business insights that can be gained by connecting Big Data to business growth, they outline how data and analytics can be used.
How to Become a Business Analyst?
The role of the business analyst is to serve as a conduit between data engineers and management executives. Therefore, they should be knowledgeable about business finances, business intelligence, and IT technologies, such as data modelling, visualization tools, and so on.
9. Data and Analytics Manager
Managers assign duties according to team skills and expertise to their teams in data science operations. In addition to management, their strengths should include SAS, R, SQL, etc.
How to Become a Data and Analytics Manager?
A strong work ethic, excellent social skills, and excellent leadership qualities are essential. You should also be good at data science technologies like Python, SAS, R, Java, etc.
Data Science Certifications
Using data to drive data driven decisions has never been more important than now. Data scientists need to be able to analyze data and communicate results. IBM's professional certificate helps individuals interested in machine learning and data science develop skills and experience relevant to their careers.
The successful completion of these courses will give you the confidence to launch into an exciting career in data science, thanks to your portfolio of data science projects.
