Multi-tasking work of data scientists

Enterprises actively use data science to become market leaders. Data comes from different sources such as the web, social media, customer reviews, internal databases and government data sets. But just storing this data doesn't help the company in any way, but uses the data people need to analyze it. Analyzing data is not easy because the trend is hidden.

The revenue of the data science industry comes from all industries at home and abroad. Last year, it only earned $1.27 billion in revenue and is expected to reach $20 billion by 2025. This sudden increase is because big data has proven to be of great value to businesses. Some uses are:

• Help understand market needs.
• Helps innovate new products and services.
• Help customers stay and be satisfied.
• Help communicate the brand to the customer.
• Help with digital and social media marketing.
• Help with real-time experiments and check business performance.

The role of data scientists:

Data scientists are data stewards who search for meaning in the collected data. Data professionals play many roles in their data to daytime activities. Since the entire data flow is a conduit for many of the steps that are connected together, the data scientist can combine them all together or instruct the experts to complete the process separately. Some of the roles they perform are:

• Conduct research and build market-related issues.
• Collect data from a variety of internal and external sources, such as networks, internal databases, data sets available on the Internet, or customer reviews on social media platforms.
• Clear and whipping data from all inconsistencies, such as blanks and incorrectly entered numbers, time zone differences, etc.
• Explore data from all directions to find any behavior patterns or trends hidden in it. To this end, a number of tools are used that are programmed for exploratory data analysis.
• Use statistical and mathematical models and tools to drill down into the data and prepare for predictive decisions.
• Build new algorithms, also known as machine learning, where data is used for automation.
• Communicate what you have learned using data visualization tools and present them in a way that management can understand.
• A correct understanding will lead to feasible decision making and finding solutions that can be applied in practice.

Different companies have different tasks queued for data analysis, but most activities are still similar.

The skills of data scientists:

Data scientists need to master some skills. But the most important thing is to have a curiosity and an analytical mindset. Searching for a question, and then like the detective sniffing the answer from a lot of data, this is not a joke. Core clicks such as patience, curiosity and situational understanding can help people succeed. The rest of the knowledge is technical and can be learned and practiced. Some of the skills required are:

• Mathematics, statistics and probability.
• Programming and coding.
• Cloud computing [Amazon S3]
• Machine learning and modeling
• Database management.
• Tools such as Python, Apache Spark and Flink, Hadoop, Pig and Hive.
• SQL, Java, C / C ++
• Industry knowledge.
• Speech and communication skills.
• Decision making skills.

Industries of all sizes and influences require experts to provide these skills and become successful data scientists. These are mandatory requirements.

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