Introduction to Data Analytics

The technique of examining data in order to gather valuable insights that can be used to guide insightful business decisions is known as data analytics. Data analytics is used to solve challenges within an organization. Patterns in a dataset will show relevant information in a specific area for example the temprature at a particular time.

Data analytics uses past data to predict the future behaviors therefore making informed decision on the information from the data.

Data Analyst

The work of a data analyst is to gather and combine information from variety of sources, to ensure accuracy and dependability, to clean up and preprocess data, to analyze exploratory data to find trends, patterns and irregularities.

A data analyst extracts raw data, organizes and analyzes the data. After interpreting the data, the data analyst will transfer their findings on what the next step should be.

Data Analysis Process

• identify the data required
• Collection of data
• Data cleaning
• Data Analysis
• Data Interpretation and visualization

Data Analysis Techniques

Data Analysis techniques and methods fall under two main types namely:

1. Qualitative Data Analysis - this method extracts data from texts or words, pictures, symbols and observations

2. Quantitative Data Analysis - this method turns raw datasets into numerical data.

Top Techniques for Analyzing Data

• Neural Network

• Cohort Analysis

• Time Series Analysis

• Factor Analysis

• Regression Analysis

• Cluster Analysis

• Data Mining

• Conjoint Analysis

• Multidimensional Scaling

• Decision Trees

• Context Analysis

• Text Analysis

Tools

Data Analysts use tools such as Microsoft Excel, Power BI, Tableau, Jupyter Notebook, Statistical Analysis System(SAS) and programming languages such as SQL, R and Python. Such tools help data analysts to carry out various tasks such as data mining, statistical analysis, database management and reporting.

Skills

The hand on skills required for one to become a data analyst are statistics, knowledge of programming languages such as SQL, R and Python and data visualization.