Data Analysis

Unlock the power of data with Techie City’s Data Analysis course.

(4.5/5)

In today’s data-driven world, businesses rely on skilled analysts to interpret and transform raw data into actionable insights. This course will teach you how to collect, clean, and analyze data using industry-standard tools and techniques. You’ll learn to create meaningful reports and visualizations that help drive business decisions.

Whether you’re a beginner or looking to enhance your data skills, this course offers hands-on training that prepares you for roles in various industries, including finance, healthcare, marketing, and more.

What You Will Learn?

Introduction to Data Analysis:

  • Understanding the role and importance of data analysis
  • Overview of the data analysis process: Collection, Cleaning, Analysis, and Interpretation
  • Key concepts: Data types, variables, and datasets

Data Collection and Cleaning:

  • Collecting data from various sources (surveys, databases, etc.)
  • Cleaning and preparing data for analysis
  • Handling missing data, outliers, and inconsistencies

Statistical Analysis and Data Exploration:

  • Descriptive statistics: Mean, median, mode, variance, and standard deviation
  • Data exploration techniques: Correlation, regression, and hypothesis testing
  • Using Excel, Google Sheets, or Python for basic statistical analysis

Data Visualization and Reporting:

  • Creating charts, graphs, and dashboards using Excel, Tableau, and Power BI
  • Building compelling data stories to communicate insights
  • Presenting findings to non-technical stakeholders

Advanced Data Analysis Techniques:

  • Introduction to SQL for querying databases
  • Exploratory data analysis (EDA) using Python or R
  • Analyzing trends and patterns in large datasets

Predictive Analytics and Machine Learning (Introduction):

  • Basics of predictive modeling and machine learning concepts
  • Understanding algorithms like linear regression, decision trees, and clustering
  • Using tools like Python (Pandas, NumPy, Scikit-learn) to build simple models

Portfolio Building:

  • Working on real-world data analysis projects
  • Creating a portfolio to showcase your analysis skills to employers
  • Preparing for job interviews in data-related roles