Become A Data Analyst
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COURSE DATES AND LOCATIONS
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INTRODUCTION
Data analytics can be compared to how you make a jigsaw puzzle. Your first step is to gather all the puzzle pieces and then fit them correctly to bring out the final picture. Similarly, in data analytics, you have to analyze data collected from several sources, clean it, and then transform it into information that humans can interpret.
Data Analytics is the method of examining datasets to find trends and draw conclusions about the information they contain. The main purpose of data analytics is to discover useful information, draw conclusions, and assist in decision making. According to the increase in data generation, the term “data analyst” has found its meaning today. However, to become a data analyst, there are a skills and a few steps that have to be followed. In this course on how to become a data analyst, you will get an in-depth understanding of what you must do to become a data analyst, unearth the skills required to bag this position and you’ll get a step closer to your dream of becoming a data analyst.
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COURSE OBJECTIVES
- Perform data analysis & visualization with Python
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Perform data analysis & visualization with Excel
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Perform data exploration and analysis with SQL
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Perform data analysis & visualization with Power BI
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Write SQL Queries to explore and analyse data
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Connect to multiple data sources with Power BI
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Clean & transform data
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Create Dashboards with Power BI
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Write SQL temporary table queries to extract and query data
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Write SQL CTE queries to extract and query data
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COURSE AUDIENCE
This course is made for :
- IT specialists
- Sales and Marketing employees
- Beginner data analysts
- Commercial excellence officers
- Brand managers
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COURSE OUTLINE
Day One
Overview of data analytics
- Importance of data analytics
- Impact on accounting
- Types of data analytics
- Diagnostic analytics
- Benefits
Day Two
Dealing with different types of data
- Terminologies in data analytics
- Qualitative and Quantitative data
- Data level of measurement
- Normal distribution of data
Day Three
Data Visualization
- Understanding of data visualization
- Commonly used visualizations
- Data visualization tools
- BI software challenges
Day Four
Data science methodology
- From business understanding to analytical approach
- From requirement to collection
- From modelling to evaluation
Day Five
Analytics framework
- Customer analytics framework
- Data preparation
- Modelling
- Graph analytics