Data Analyst-PythonETLSSISSSRSSSASMicrosoft SQLPowerBI

Analyse Data with Python

Visualise Data with Python

Clean Data with Python

Create an ETL Process

Create SSIS Package to Extract, Transform and Load Data

Create reports with SSRS

Create a Tabular Model with SSAS

Create Key Performance Indicators - KPI's

Create calculated columns

Creating measures

Analyse data with SSAS

Analyse data with Microsoft SQL (T-SQL)

Connect to multiple data sources with Power BI

Analyse data with Power BI

Visualise data with Power BI

Analyse and visualise data on SQL Server Database with Power BI

SQL Server is required( Installation is covered in the course)

Python & Jupyter Notebook is required( Installation is covered in the course)

Power BI is required( Installation is covered in the course)

SQL Server Analysis Services (SSAS) is required( Installation is covered in the course)

Data is everywhere. Everything you do online and in your daily life generates data—and it’s a valuable business resource. In fact, 94% of companies say data and analytics are essential to their growth. As we move toward greater digitization, there is an ever-increasing demand for professionals who can turn information into actionable insights. A data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of spent with data but entails communicating findings too. Here’s what many data analysts do on a day-to-day basis:Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed. Model data: This entails creating and designing the structures of a database. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears.Interpret data: Interpreting data will involve finding patterns or trends in data that could answer the question at hand.Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested partiesDuring the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry include:Microsoft ExcelGoogle SheetsSQLTableauR or PythonSSASSSRSSSISETLMicrosoft Power BIJupyter Notebooks

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