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Applications and Role of Data Science
A company has to deal with a large amount of data such as salaries, employee data, customer data, customer feedback, etc. This data can be in both unstructured and structured form. A company would always want this data to be simple and complete so that they can make better and accurate decisions and future policies. This is where data science comes in handy.
Data science helps customers make the right decisions based on the right information gleaned from a huge amount of messy data. Data scientists use their formidable skills in mathematics, business, programming and statistics to clean and organize data into useful information and reveal hidden patterns, trends and correlations.
Applications of data science
It has now become an inevitable and integral part of industries such as risk management, market analysis, market optimization, fraud detection and public policy, among others. Data science through the use of statics, machine learning and predictive modeling helps industries solve various problems and achieve quantifiable benefits. There are many reasons to opt for a data course as a career option. The following applications help us understand this better:
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It helps businesses understand customer behavior and inclinations in a very empowering way. It helps them connect with customers in a more personalized way and ensure better customer services.
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It helps brands use data in a comprehensive way to communicate their message in an engaging and compelling way with their target audience.
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The results and findings of data science can be implemented in almost all sectors such as healthcare, education, travel, among others, helping them to tackle the challenges of their field in a more effective way .
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Big Data is an emerging field that is helping organizations address HR, resource management, and IT issues in a strategic way by using tangible and intangible resources.
Data scientist is one of the main positions in an organization. They open new grounds for experimentation and research in the organization. Some of the direct duties of a data scientist are:
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Link the new data with the old to offer new products that meet the aspirations of the target audience.
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Interpret weather conditions and, accordingly, reorient the supply chain.
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To improve the speed of evaluation and integration of the data set.
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To reveal anomalies and fraud in the market.
An overview of the data science course
The data science course is over 160 hours of training with experienced faculty working in leading organizations to keep you up to date with the latest technologies. An overview of the course is as follows:
- Mathematics and Statistics: This is a comprehensive topic in the data science course and includes integration, differentiation, differential equations, etc. Statistics covers inferential statistics, descriptive statistics, chi-square tests, regression analysis, etc.
- Programming language: One can select from a variety of programming languages such as Python, C++, Matlab, Hadoop, etc.
- Data network and data management: This part deals with data mining, cleaning and management using MySQL, NoSQL, Cassandra, etc.
- Machine learning: This includes supervised and unsupervised learning, testing, reinforcement learning, model evaluation and validation.
- Data analysis and visualization: This part teaches how to use plotting libraries for programming languages such as seaborn in python, plotly, ggplot2 in R, matplotlib, etc. It also involves using Excel, Tableau and D3.js for data visualization.
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