Introduction to Career in Data Science

The impact of ‘Information Technology is changing everything about science. Lots of data is gathered daily from across the world. As data grows, so does the expertise needed to manage it, analyze this data, and make good insights into this data; the data science discipline has emerged as a solution.

In simpler terms, careers in Data Science convert or extract the data in various forms into knowledge. So that the business can use this knowledge to make wise decisions to improve the business. Companies have become intelligent enough to push and sell products using data science.

Education Skills required for Data Science

Knowledge about how to build data products and visualization to make data understandable, Domain Knowledge, Data Mining, Machine learning, Algorithms, Big Data processing, Structured Unstructured Data(SQL and NoSQL DBs), Coding, Probability and Statistics

Career Path in Data Science

As the present economy and business become more driven by digital activity, data has begun to play a vital role in many industries. But one should be aware of what Data Science is and what Careers in the the data science field look like.

Careers in Data Science is a very large field; it contains many other fields such as big data Hadoop, data mining, warehousing, analytics, etc. Data is the primary factor for all these areas. Most data science positions involve storing, organizing, and analyzing data sets. The Data Science career path revolves around the combinations mentioned above, such as Data Analyst, Data Scientist, Data Architect, Machine Learning, etc.

Job Positions or Applications Areas

Let’s look at some of the Job positions we have in Data Science.

1. Data Analyst – Responsibilities

A Data Analyst is responsible for transforming data sets into practical forms such as presentations and reports.
Extracting the knowledge from the raw data is the Data Analyst’s main duty, including organizing messy data, finding insights from consumer data sets, etc.
A Data Analyst is responsible for creating dashboards, graphs, and visualization on data sets.
The responsibilities also include identifying, evaluating, and implementing services and tools from external sources to support data cleaning and validation.

Skills required for Data Analyst

Knowledge about analytics as it is self-explanatory; this includes the the ability to gather, visualize, and analyze all forms of information in data sets.
Knowledge about Numeracy includes figures and numbers.
Knowledge about database systems and spreadsheets.
A Data Analyst must also have good communication, programming, and algorithms skills.

2. Data Scientist- Responsibilities

A data scientist should know about most of the techniques and methods related to Data Science, such as statistics.
A data scientist makes value out of raw data sets by extracting and interpreting data. A data scientist’s responsibility is to design and implement the process used for data modeling, data mining, and research.
Additionally, the Data scientist is responsible for modeling standards, data mining architectures, reporting, and data analysis methodologies.
Data Scientist responsibilities usually include creating various Machine Learning-based tools or processes within the organization, such as automated lead scoring systems or recommendation engines.
Data Scientist duties include developing prototypes, proof of concepts, custom analysis, algorithms, and predictive models.

Skills required for Data Scientist:

Degree in Statistics or Computer Science Engineering.
Knowledge about Programming languages and data-driven languages such as SQL, Hive, Spark, etc
Knowledge about Statistical analysis, data analytics, reporting tools, and visualization.
Strong communication skills and data Intuition.

3. Data Architect Responsibilities

A Data Architect is the one who develops the underlying architecture for analyzing and processing the data in the way the organization wants it to process.
A Data Architect is responsible for creating the project blueprint using all the applications and tools by integrating and centralizing.
A Data Architect should ensure that the data environment is reliable (It shouldn’t contain vulnerability), robust, stable, and secure.

Skills required for Data Architect:

Knowledge about the tools involved in the eco-system to design the blueprint and architecture
Knowledge of data warehousing, databases, and data
Knowledge of programming and query languages such as SQL, Hive, Spark, etc.
Working experience with ETL, BI tools, and spreadsheet

The above-mentioned job profiles are only a few options in Data Science; there are various job positions or roles in Data Science such as Data Engineer, Business Analyst, Machine Learning Engineer, Database Administrator, Statistician, etc.

Salary

1. Data Analyst

According to payscale.com, the average salary for a Data Analyst is $57 675 per year for the first five years to 10 years in this position. Pay increases as the experience increases in the field.
The skill set also matters for the pay to increase. Skills that increase pay for this job, mostly R, Python, and Tableau Software.

2. Data Scientist

According to glassdore.com, the average salary for a Data Scientist is $120,931 per year.
Again the payment will depend on the strong skill set and experience in the industry.

3. Data Architect

According to glassdore.com,, the average salary for a Data Architect is $112,764 per year in the United States.
The skills that can increase the pay for the Data Architect job are Data Mining, Data Warehousing,, and Big data analytics.

4. Data Engineer

According to payscale.com,, A Data Engineer earns an average salary of $90,286 annually.
Apache Spark, Hadoop, Java, Scala, Data Warehouse,, and Data Modeling is the highest paying skills connected with this job. Experience for Data Engineer has a moderate effect on income.

Conclusion

Careers in Data Science are rapidly growing every day, as do the opportunities in it. As mentioned earlier, the positions are very famous due to the demand for Big Data. Careers in Data Science include several options, as mentioned above. There is a high demand for the Data science skills set in the market,, and the pay is also very good.

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