What is Data Science?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

Why is data science important?

Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities.

Future of data science

Artificial intelligence and machine learning innovations have made data processing faster and more efficient. Industry demand has created an ecosystem of courses, degrees, and job positions within the field of data science. Because of the cross-functional skillset and expertise required, data science shows strong projected growth over the coming decades

What is the data science process?

A business problem typically initiates the data science process. A data scientist will work with business stakeholders to understand what business needs. Once the problem has been defined, the data scientist may solve it using the OSEMN data science process:

O – Obtain data
S – Scrub data
E-Explore data
M-Model data
N – Interpret results

Data Science Modules

  • • Module 1 – Data Science virtual environment
  • • Module 2 – Brief content of Python
  • • Module 3 – Python with Data Science library
  • • Module 4 – Python for Data Analysis - NumPy
  • • Module 5 – Python for Data Analysis - Pandas
  • • Module 6 – Python for Data Analysis - Matplotlib
  • • Module 7 – Python for Data Analysis - Seaborn
  • • Module 8 – Pandas Built-in Data Visualization
  • • Module 9 – Python for Data Visualization - Geographical Plotting
  • • Module 10 – Data Capstone
  • • Module 11 – Introduction to Machine Learning
  • • Module 12 – Linear Regression
  • • Module 13 – Cross Validation and Bias-Variance Trade-Off
  • • Module 14 – Logistic Regression
  • • Module 15 - K Nearest Neighbors
  • • Module 16 – Decision Trees and Random Forests
  • • Module 17 – Support Vector Machines
  • • Module 18 – K Means Clustering
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