Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage.
Frame the problem: Who is your client? What exactly is the client asking you to solve? How can you translate their ambiguous request into a concrete, well-defined problem?
Collect the raw data needed to solve the problem: Is this data already available? If so, what parts of the data are useful? If not, what more data do you need? What kind of resources (time, money, infrastructure) would it take to collect this data in a usable form?
Process the data (data wrangling): Real, raw data is rarely usable out of the box. There are errors in data collection, corrupt records, missing values and many other challenges you will have to manage. You will first need to clean the data to convert it to a form that you can further analyze.
Explore the data: Once you have cleaned the data, you have to understand the information contained within at a high level. What kinds of obvious trends or correlations do you see in the data? What are the high-level characteristics and are any of them more significant than others?
Perform in-depth analysis (machine learning, statistical models, algorithms): This step is usually the meat of your project,where you apply all the cutting-edge machinery of data analysis to unearth high-value insights and predictions.
Communicate results of the analysis: All the analysis and technical results that you come up with are of little value unless you can explain to your stakeholders what they mean, in a way that’s comprehensible and compelling. Data storytelling is a critical and underrated skill that you will build and use here.
Natural Language Processing
Some applications include Email filters, Virtual assistants, voice assistants, or smart speakers, Online search engines, Predictive text and autocorrect, Monitor brand sentiment on social media, Sorting customer feedback, Automating processes in customer support, Chatbots, Automatic summarization of articles, books, language translation, natural language generation etc.
Computer Vision
Face recognition, object detection and tracking, industrial defect detection. Identity verification using photographs of IDs. Self-driving cars use computer vision to detect and track objects.
Having clean data will ultimately increase overall productivity and allow for the highest quality information in your decision-making. Benefits include:
Removal of errors when multiple sources of data are at play.
Fewer errors make for happier clients and less-frustrated employees.
Ability to map the different functions and what your data is intended to do.
Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
Using tools for data cleaning will make for more efficient business practices and quicker decision-making.
Google Apps Script is a scripting language based on JavaScript that lets you customize and extend products such as Docs, Sheets, Slides, and Forms.
Add custom menus, dialogs, and sidebars to Google Docs, Sheets, and Forms.
Write custom functions for Google Sheets.
Publish web apps—either standalone or embedded in Google Sites.
Interact with other Google services, including AdSense, Analytics, Calendar, Drive, Gmail, and Maps.
Build add-ons to extend Google Docs, Sheets, Slides, and Forms, and publish them to the Add-on store.