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In simple terms, a data scientist’s job is to analyze data for actionable insights.

Some specific tasks include:

1. Identifying the data-analytics problems that offer the greatest opportunities to the organization

2. Determining the correct data sets and variables

3. Collecting large sets of structured and unstructured data from disparate sources

4. Cleaning and validating the data to ensure accuracy, completeness, and uniformity

5. Devising and applying models and algorithms to mine the stores of big data

6. Analyzing the data to identify patterns and trends

Interpreting the data to discover solutions and opportunities

7. Communicating findings to stakeholders using visualization and other means.

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Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google Assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning:

1. Automatic language translation.

2. Medical Diagnosis.

3. Stock market trading.

4. Online fraud detection.

5. Virtual personal assistant.

6. Email spam and malware filtering.

7. Self-driving cars.

8. Product recommendations.

9. Traffic prediction.

10. Speech recognition.

11. Image recognition.

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Tools to be used by a developer.

DATA SCIENCE ROLES IN:

1. FINANCE:
Among the many ways that accountants apply data science techniques are to monitor and enhance accounting and financial processes, calculate the risk related to strategic decisions, and anticipate and meet their customers' expectations.

2. BANKING:
Data Science is used in banking to control various financial activities and determine the appropriate pricing for financial products. There are two types of risk modeling. One is Credit Risk Modelling and another is Investment Risk Modelling.

3. TRANSPORT:
Reducing freight costs through delivery path optimization. Dynamic price matching of supply to demand. Warehouse optimization. Forecasting demand.

4. E-COMMERCE:
Data science powers predictive forecasting using various data sources, such as the historical data of sales, economic shifts, customer behavior, and searches. This empowers e-commerce companies by promoting relevant products to potential buyers.

5. HEALTH CARE:
Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

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TOP 5 VISUALIZATION TOOLS FOR DATA SCIENTISTS:

1. TABLEAU
Tableau is among the master software in Data Visualization. It can handle a large amount of data that are also frequently changing and used in Big Data operation. It creates efficient graphics and visualization, making the team understand data more easily. It lets one create charts, tables, maps, and other graphics as required.

2. MICROSOFT POWER BI
Power BI is the software solution developed by Microsoft which provides business intelligence and analytic needs. It is an online service that provides a connection to data even through third-party software and services.

3. PLOTLY:
Plotly is an open-source module of Python which is used for data visualization and supports various graphs like line charts, scatter plots, bar charts, histograms, area plots, etc.

4. EXCEL:
You can display your data analysis reports in a number of ways in Excel. However, if your data analysis results can be visualized as charts that highlight the notable points in the data, your audience can quickly grasp what you want to project in the data.

5. SISENSE:
Sisense is a business analytics platform that cleans your data, provides interactive visual analytics, and delivers insights. Sisense provides customized dashboards based on your industry.

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All about DEEP LEARNING 🧑‍💻✨

Data analytics and data science are often used interchangeably. However, if you take a closer look, the two fields have quite a number of difference in data sets.

1. DATA ANALYTICS is the process which delves more into helping you understand aspects of your business that you might not know. This is why it is a better option for those who are looking to drive innovation within the company.

2. DATA SCIENCE is a multidisciplinary field that consists of different processes. Used mostly for large sets of raw and structured data, it can predict trends in your data, find potential problems, as well as discover opportunities for your business.

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Data science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information.

Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas.

1. Domain Knowledge
Most people thinking that domain knowledge is not important in data science but it is very very important.

2. Math Skills
Math skill is very very important if you are landing to the data science world.

3. Computer Science
Computer science plays a major role in data science. Whether it may draw a complex chart or implement those complex machine learning algorithms it’s not possible without a programming language like Python and R.

4. Communication Skill
It includes both written and verbal communication.

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Following are the Important Elements of Data Science that are used in Data Science.
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Data science skills you have and what you want to add in...
Statistical analysis, Deep Learning, Data visualization, SQL , Machine learning.

According to the DATA SCIENCE VENN DIAGRAM, Machine learning involves the knowledge of Computer programming and Math but without any domain expertise.

Data Science is made up of mainly three things and represented in the form of a Venn Diagram indicating their individual roles.

These basic things are:

1. Statistical Mathematics

2. Computer Science

3. Business

Data Science is in the middle of this Venn Diagram combining all these skills. The Data Science Venn Diagram gives a visual representation of how these areas work together in Data Science.
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