1. Learn the fundamentals of databases and statistics. 2. Utilize lookup and pivot tables 3. Make easy macros that streamline your productivity.

1. Statistics and data analytics basics. 2. How to use data to make predictions and decisions. 3. How organizations integrate data analytics into their procedures.

1. Handle many data types, including text, floats, and integers. 2. Use functions, loops, and conditional statements to manage the flow of your applications. 3. Utilize object-oriented programming to reuse and streamline code. 4. Utilize Numpy and Pandas to analyze tabular data. 5. Use Matplotlib to make graphs and visualizations. 6. Utilizing scikit-learn, make predictions using linear regression.

1. Utilize DBeaver’s graphical user interface to explore the data (tables, columns, rows, etc.) contained in a database. 2. To get information from tables in a database, create SQL queries. 3. Use JOIN statements to combine data from many tables. 4. To obtain the precise information you require, filter, group, and sort your data. 5. Subqueries, string functions, and IF-Else logic using CASE are examples of advanced approaches. 6. When to query tables indirectly and when to use views and user-defined functions.

1. Learn Python’s syntax and how to write programs. 2. Learn how to schedule the execution of your programs. 3. How to deal with mistakes.

1. Create and deliver a data story. 2. Collect and modify data from several sources. 3. Through exploratory data analysis, locate data stories. 4. Utilize Pandas and NumPy to manipulate data. 5. Utilize the cutting-edge Python visualization tools Plotly and Dash. Construct a dashboard. 6. Use the best dashboard design practices to produce high-quality data science solutions. 7. Launch your project and install the dashboard on a working server.

1. How to use the Pandas library to balance and clean your data. 2. Utilizing the scikit-learn package to apply machine learning algorithms like logistic regression and random forest. 3. Choosing quality features to feed your algorithms. 4. Separating data into training, test, and cross-validation sets appropriately. 5. Important theoretical ideas including bias, variance, and overfitting. 6. Evaluating how well your machine learning models are performing.

1. The creation and formatting of data visualizations. 2. You can organize, sort, and filter your data. 3. Create dynamic stories and interactive dashboards. 4. Work with custom charts and data mapping. 5. Publish and distribute the finished visuals.

1. Master topics such as Integration and Data Visualization 2. Perform Data Binding and Formatting 3. Learn about Power View, Power Pivot, and Power Map 4. Comprehending DAX and SSBI 5. Understand and use Data Gateways, Power BI Report Server, and Content packs 6. Explore Power BI Q&A 7. Using and Adding Custom Graphics 8. Master Power BI Embedded and Workspace