Data Analysis for Beginners | An Easy Guide
Data Analytics vs Data Science
Let’s first help you to define the difference between these two terms. Data analytics is a strategy-based science that can help you analyze raw data, allowing for trend detection and answering questions. Data analysis helps you to draw the right conclusions from a huge batch of data. Moreover, it helps you to use various techniques where raw data gets converted and becomes successful in a way that proves beneficial for companies and organizations for analyzing the right metrics.
What Are the Different Types of Data Analysis
Descriptive Analysis: Descriptive Analysis gives an insight into the analysis and different data-related features. It utilizes critical techniques for data analysis, aggregation, and mining. Out of these, the most important thing is gathering and sorting data using data aggregation. Following this, data mining is done using patterns analysis.
Predictive Analysis: Predictive analysis is done by looking at historical data to find the likely outcomes. This process also involve descriptive analysis. You need a large amount of data to utilize predictive models to provide accurate outcomes.
Diagnostic Analysis: Issues, problems, inaccuracies, and anomalies that can negatively impact a business’s performance. Digging out errors and problems is a big part of data analysis. Various strategies can help identifying issues such as clustering and anomaly detection. Ultimately, the goal here is to discover why things are underperforming.
Prescriptive Analysis: This involves the combination of various kinds of above analysis; this technique involves actional insights rather than data monitoring. One can gather the data through predictive and descriptive models and using the principles of maths and science.
What’s the Process of Data Analysis
- Collecting Requirements
- Data Collection
- Data Processing
- Data Cleaning
- Data Analysis
This video will show you all the steps you need to complete an analysis.
What’s Skills Do You Need as a Data Analyst?
Data Visualization( Required ): Data visualization helps engage customers while presenting the data. Creating charts and tables is vital, allowing the audience or clients to understand all nuances in complex data. Also, data visualization enables you to progress well in your career quickly.
- Simple visual theory
- Univariate Charts
- Bivariate Charts
Data Cleaning Skills (Required) : The raw data you get will be inconsistent and full of anomalies and errors. That’s why it is essential to get data cleaning skills for a good analyst. “Garbage in equals Garbage Out!”
- Text clean
- Formatting of data types
- Outlier detection
Microsoft Excel( Required ): These days, everyone has a basic understanding of Excel. Analysts need to know about advanced methods.
- Pivot Tables
- Power Query
- Goal Seek
- Analysis Toolkpak
Statistical Knowledge ( Required): Don’t be intimidated by statistics . Its a vital aspect of data analysis. People with good statistical knowledge can often solve analysis issues quickly and accurately. Using the right aggregations can help you tell correct story with data.
- Aggregations ( Mean, Median, Mode, Standard Deviation)
- A/B Testing
SQL: As an analyst you will to extract data from every source different sources. Often the most used place to store data is in a database. Analyst use SQL or Structured Query Language to access and retrieve this data.