Best Introduction to Data Science

Simply put, data science is the extraction of knowledge from data collected using a variety of methods. Data scientists take complex business problems, compile findings from them, transform them into data, and use that data to solve problems. What does this mean for you? Where and how do I start? Let’s check out below:

What Does Data Scientist Do?

Data scientists work in a variety of fields. Each is important and requires specific knowledge to solve the problem. These areas include data collection, preparation, extraction, modeling, and model maintenance. Data scientists take raw data and use machine learning algorithms to transform it into a treasure trove of information that answers questions for companies looking for solutions to their queries. This introductory data science tutorial walks you through each area, starting with:

Data Collection: Here, data scientists retrieve data from all raw sources such as databases and flat files. The data is then integrated, transformed into a uniform format, and collected into a system that makes it easy to extract information from the data, a so-called “data warehouse.” This step is also known as ETL and can be performed using tools such as Talend Studio, DataStage, and Informatica.

Data Preparation: This is the most important phase where data scientists spend 60% of their time.
Because data is often “dirty” or useless and needs to be scalable, productive, and meaningful.
There are actually 5 sub steps here:

  1. Data Cleaning
  2. Data Transformation
  3. Handling Outliers
  4. Data Integration
  5. Data Reduction

Data Mining: Here, data scientists uncover patterns and relationships in data to make better business decisions. This is a discovery process that yields hidden useful knowledge and is commonly referred to as exploratory data analysis. Data mining helps predict future trends, identify customer patterns, make decisions, quickly detect fraud, and select appropriate algorithms. Tableau is great for data mining.

Model building: This goes beyond simple data mining and requires building machine learning models. Models are built by selecting machine learning algorithms that fit the data, problem statement, and available resources.

Best Introduction to Data Science

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