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Future of Data Science

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Brief Description:Data Scientist has to use statistical methods. It includes mix modeling, predictive response modeling. Also, optimization techniques to meet client business needs.

They have to develop and install statistical tools. it helps in building predictive models. These models support clients in customer marketing and demand generation initiatives.

Data Scientist collaborates with internal consulting teams to set analytic objectives, approach. Also, work plans to provide programming and analytic support to internal consulting. It also provides statistical procedures utilizing SAS and Microsoft Office.

Additionally, strong communication and problem-solving skills are essential to most jobs.

Again, keep one thing in mind. The specific requirements will vary according to the company and position.

Essential Skills and Training in Data Science
  • Some common skills for data scientist across most positions:
  • Multi-variable Calculus and Linear Algebra Software Engineering.
  • Statistics of Data Mining.
  • Machine Learning PL such as Python, C/C, Java.
  • Knowledge of Databases such as SQL Platforms such as Hadoop.
Additionally, skill is needed:
  • Strong communication and problem-solving skills are essential to most jobs.
  • Also, specific requirements will vary according to the company and position.
Data Science Vs Machine Learning

Machine learning and statistics are part of data science. Also, Machine learning itself defines that the algorithms depend on some data. We use it as a training set, to fine-tune some model or algorithm parameters. In particular, data science also covers:

  • data integration.
  • distributed architecture.
  • automating machine learning.
  • data visualization.
  • dashboards and BI.
  • data engineering.
  • deployment in production mode.
  • automated, data-driven decisions.
Why Machine Learning for the Future of Data Science?

One needs to think a little bit about the relationship between data science and machine learning. Data science includes machine learning.

Machine learning–

It is the ability of a machine to generalize knowledge from data — call it learning. Without data, there are little machines can learn. To push data science to increase relevance, a catalyst is an important thing. While it helps in increasing machine learning usage in different industries. As machine learning is good because it has data within it. It also has the ability to consume algorithms in it. My expectation is that moving forward basic levels of machine learning. It will become a standard need for data scientists.

Future of Data Science and Data Scientist

After the next 5 years, they will develop the ability to use all sorts of data in real-time. For the needs of the future, it will spark the emergence of new data science paradigms.

We can use more data to drive key business decisions. We will enable innovations like “Deep Learning”. it allows for accurate predictions and decision making. Further, modern applications have brought to fore new statistical paradigms.

The most important thing:
Skilled data scientists; statisticians, and, business analysts will be the key to unlocking the endless possibilities of big data.