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Data Analysis

Python 

  • Python Basics
    • Python installation and setup
    • Introduction to Python programming
    • Basic syntax, variables, and data types
    • Data Structures and Libraries
      • Lists, tuples, dictionaries, and sets
      • Introduction to libraries: NumPy, Pandas, Matplotlib, Seaborn
    • Data Manipulation with Pandas
      • DataFrames and Series
      • Reading and writing data (CSV, Excel, SQL databases)
      • Data cleaning and preprocessing
      • Merging, joining, and concatenating DataFrames
    • Data Visualization with Matplotlib and Seaborn
      • Basic plotting with Matplotlib
      • Advanced visualization with Seaborn
      • Customizing plots and charts

Excel 

    • Excel Basics

      • Excel interface and features
      • Basic functions and formulas
      • Data entry and formatting

    • Data Analysis with Excel

      • Pivot tables and pivot charts
      • Data cleaning and manipulation
      • Advanced functions (VLOOKUP, INDEX-MATCH, etc.)

    • Data Visualization in Excel

      • Creating charts and graphs
      • Conditional formatting
      • Using Sparklines for data trends

Course Duration

120 hours

Note: The course may be subject to change based on the student’s understanding of the course and the instructor’s discretion.

SQL

    • Introduction to SQL

      • SQL databases and tables
      • Basic SQL syntax
      • Data types and constraints

    • Querying Databases

      • SELECT statements
      • Filtering and sorting data
      • Aggregate functions (SUM, AVG, COUNT, etc.)

    • Advanced SQL

      • Joins (INNER, LEFT, RIGHT, FULL)
      • Subqueries and nested queries
      • Common Table Expressions (CTEs)
      • Window functions

Power BI

    • Introduction to Power BI

      • Power BI interface and features
      • Connecting to data sources
      • Data transformation with Power Query

    • Data Modeling in Power BI

      • Creating and managing relationships between tables
      • Introduction to DAX (Data Analysis Expressions)
      • Creating calculated columns and measures
      • Building and managing data models

    • Building Reports and Dashboards

      • Creating and customizing visualizations
      • Using slicers and filters for interactive analysis
      • Designing and formatting reports
      • Building interactive dashboards

    • Advanced Features in Power BI

      • Using Power BI Service for collaboration and sharing
      • Scheduling data refreshes
      • Implementing row-level security
      • Power BI Mobile for on-the-go access

    • Integration and Automation

      • Integrating Power BI with other tools (Excel, SharePoint, etc.)
      • Using Power Automate for workflow automation
      • Embedding Power BI reports in applications

Project

  • Students will work on a real-world data analysis project
  • Apply the skills learned throughout the course
  • Present findings and visualizations to the class

Data Analysis for Switching Careers in the IT Field

If you are looking to switch careers into the IT field, data analysis is a promising and high-demand area to consider. Proficiency in key tools and languages like Python, Power BI, Tableau, SQL, and Excel can open up a wide range of opportunities.

Why Data Analysis?

Data analysis involves examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. With the increasing amount of data generated every day, companies need skilled data analysts to help them make sense of this data and gain valuable insights. Here are the key tools and languages you should focus on to build a successful career in data analysis.

Python

Python is a versatile, high-level programming language that is widely used in data analysis for its simplicity and power. Here are some reasons why Python is essential for data analysis:

– **Easy to Learn:** Python’s simple and easy-to-read syntax makes it an excellent choice for beginners.
– **Powerful Libraries:** Python has a wealth of libraries such as Pandas, NumPy, Matplotlib, and SciPy that are specifically designed for data analysis tasks.
– **Community Support:** A large and active community of developers constantly contributes to a vast ecosystem of tools and resources.
– **Versatility:** Beyond data analysis, Python can be used for web development, automation, machine learning, and more.

Power BI

Power BI is a business analytics tool by Microsoft that allows you to visualize your data and share insights across your organization. Here are some benefits of using Power BI:

– **Interactive Visualizations:** Create interactive reports and dashboards with a variety of visualizations.
– **Integration:** Seamlessly integrates with other Microsoft products and a wide range of data sources.
– **Ease of Use:** Intuitive interface and drag-and-drop features make it accessible for users with varying levels of technical expertise.
– **Collaboration:** Share reports and dashboards easily with others in your organization.

Tableau

Tableau is another powerful data visualization tool that helps you to see and understand your data. Here’s why Tableau is a valuable tool for data analysts:

– **User-Friendly:** Easy to use with a drag-and-drop interface, allowing you to create complex visualizations without extensive coding.
– **Advanced Analytics:** Offers advanced analytics features like trend analysis, forecasting, and statistical analysis.
– **Integration:** Connects to various data sources, including databases, spreadsheets, and cloud services.
– **Interactive Dashboards:** Build interactive dashboards that provide deeper insights and can be shared across teams.

SQL

Structured Query Language (SQL) is the standard language for managing and manipulating databases. Proficiency in SQL is crucial for data analysis for the following reasons:

– **Data Extraction:** Efficiently retrieve data from relational databases.
– **Data Manipulation:** Perform complex queries to filter, sort, and aggregate data.
– **Database Management:** Understand how databases work and manage data effectively.
– **Compatibility:** SQL knowledge is transferable across various database systems like MySQL, PostgreSQL, SQL Server, and Oracle.

Excel

Excel is a fundamental tool for data analysis and is widely used in many industries. Here are some reasons why Excel remains important:

– **Data Handling:** Manage large datasets with features like pivot tables, filters, and sorting.
– **Formulas and Functions:** Perform calculations and data manipulations with built-in formulas and functions.
– **Visualization:** Create charts, graphs, and other visual representations of data.
– **Accessibility:** Widely used and easy to learn, making it a versatile tool for many data analysis tasks.

Conclusion

Switching to a career in data analysis is a smart move, given the growing demand for data-driven decision-making in businesses. By gaining proficiency in Python, Power BI, Tableau, SQL, and Excel, you’ll be well-equipped to analyze and interpret data, creating valuable insights that can drive business success. These tools and languages are essential in the toolkit of any aspiring data analyst, and mastering them will significantly enhance your career prospects in the IT field.

 

Career Opportunities

Python is used in a wide range of industries, including finance, healthcare, e-commerce, and more. Some of the most in-demand jobs for Python developers include:

Data Scientist: Python is widely used in data science and machine learning, and data scientists use it to analyze and interpret data.

Web Developer: Python is used to build web applications and can be used with web development frameworks such as Django and Flask.

Software Developer: Python is used to build software applications and can be used with libraries and frameworks such as NumPy, Scikit-learn, and TensorFlow.

Network Automation Engineer: Python is widely used for network automation and can be used to automate tasks and manage network devices.

Overall, learning Python is a great way to switch careers and open up new opportunities in the IT field. With the right skills and experience, you can land a high-paying job with a great company.

In conclusion, Python is a powerful, versatile language that can help you to switch careers in the IT field. With the right skills and experience, you can land a high-paying job with a great company. And it is important to have a good understanding of general programming concepts, related technologies, and tools and work on projects to develop a portfolio. This way you can showcase your skills to potential employers and open up new opportunities in the industry.

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