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Data Analytics Level 1

Data Analytics Track

Data Analytics:
Level 1

4.8 (1,230 reviews)

Step into the world of Data Analytics with our comprehensive course designed for aspiring analysts and data-driven professionals. Master analytics fundamentals, Excel for data processing, SQL for database querying, Python programming, NumPy for numerical computing, and Pandas for data manipulation to build your first complete data analysis pipeline.

Created by Avanteia
12,580 Total Enrolled
15 Sep 2024 Last Updated
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Data Analytics Level 1 Course
2 Months Duration
Certificate On Completion
Beginner Level
6 Modules Syllabus
2 Months Duration
English Language
Certificate Included
Introduction

Overview

This Level 1 Data Analytics course takes you from analytics fundamentals to programmatic data manipulation. You will learn core statistics and analytics workflows, master Excel for data processing and dashboard building, write SQL queries for relational databases, build a solid Python programming foundation, perform numerical computing with NumPy, and manipulate structured data with Pandas — all with guided hands-on projects.

Data Analytics Excel SQL Python NumPy Pandas
Goal

Learning Outcome

Collect, clean, analyze, and manipulate data using Excel, SQL, Python, NumPy, and Pandas to generate actionable business insights and build end-to-end data analysis pipelines.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on labs included.

  • What is Data Analytics? Descriptive, Diagnostic, Predictive, Prescriptive Analytics
  • The Analytics Workflow: Collect, Clean, Analyze, Visualize, Communicate
  • Data Types, Data Structures, and Sources of Data
  • Core Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Probability Basics, Distributions, Correlation vs Causation
  • Overview of the Analytics Toolchain: Excel, SQL, Power BI, Python
Hands-on Lab
Analytics Workflow Demo Statistics Calculation Exercise Data Types Classification
  • Excel Interface, Data Entry, Formatting, and Data Validation
  • Core Formulas: Logical, Lookup (VLOOKUP/XLOOKUP), Text, Date Functions
  • Sorting, Filtering, Conditional Formatting, and Data Cleaning Techniques
  • PivotTables and PivotCharts for Summarizing Large Datasets
  • What-if Analysis: Data Tables, Goal Seek, Scenario Manager
  • Power Query for Importing, Transforming, and Merging Data
  • Dashboard Building with Charts, Slicers, and KPI Cards
Hands-on Lab
Excel Formulas Practice PivotTable Analysis Sales Dashboard Build
  • Relational Database Concepts: Tables, Keys, Schemas, Normalization
  • Writing SELECT Queries: Filtering, Sorting, and Aliasing
  • Aggregate Functions and GROUP BY / HAVING for Summarization
  • Joins: INNER, LEFT, RIGHT, FULL, and Self Joins
  • Subqueries, CTEs (WITH Clause), and Window Functions
  • Data Cleaning in SQL: CASE Statements, NULL Handling, String Functions
  • Views, Indexes, and Query Performance Basics
Hands-on Lab
SQL Query Writing Database Joins Exercise Business Insights Query
  • Setting up Python with Anaconda and Jupyter Notebook
  • Variables, Data Types, Operators, and Control Flow
  • Functions, Loops, and List/Dict Comprehensions
  • Working with Strings, Lists, Tuples, Dictionaries, and Sets
  • File Handling: Reading and Writing CSV/Excel Files
  • Introduction to Python Libraries for Data Analytics
Hands-on Lab
Python Basics Exercise Control Flow Programs File Handling Practice
  • NumPy Arrays vs Python Lists: Performance and Use Cases
  • Array Creation, Indexing, Slicing, and Reshaping
  • Vectorized Operations, Broadcasting, and Universal Functions
  • Aggregation Functions: Sum, Mean, Std, Min/Max Along Axes
  • Sorting, Filtering, and Boolean Masking
Hands-on Lab
NumPy Array Operations Vectorized Computation Numerical Analysis Exercise
  • Series and DataFrame Objects: Creation and Inspection
  • Importing Data from CSV, Excel, and SQL Databases
  • Data Cleaning: Handling Missing Values, Duplicates, and Outliers
  • Filtering, Sorting, Grouping (groupby), and Aggregation
  • Merging, Joining, and Concatenating Datasets
  • Pivot Tables and Cross-Tabulations in Pandas
Hands-on Lab
DataFrame Creation Data Cleaning Pipeline Groupby Analysis
Results

What You Will Learn

Analytics Fundamentals

Understand analytics types, workflows, and core statistics for data-driven decision making.

Excel & SQL

Process data in Excel with PivotTables and Power Query, and query databases using SQL.

Python Programming

Write Python code for data processing, automation, and reading structured data files.

NumPy & Pandas

Perform numerical computing with NumPy and manipulate structured data with Pandas DataFrames.

Testimonials

What Our Students Say

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