Certified Data Analyst Specialist
Duration: 50 Hours | 4 Months
A Certified Data Analyst Specialist is proficient in extracting, cleaning, analyzing, and interpreting data to support strategic decision-making. Key responsibilities include data wrangling, creating visualizations, performing statistical analyses, and developing actionable insights. This role focuses on leveraging data to improve business processes, identify trends, and enhance organizational efficiency.
Prerequisites
A high school diploma is required.
Basic knowledge of mathematics and statistics is recommended.
Familiarity with tools like Excel, SQL, or Python is an advantage but not mandatory.
Strong analytical skills, logical reasoning, and attention to detail are essential for success in this program.
Course Objectives
The Certified Data Analyst Specialist Training Program is designed to provide participants with the skills to collect, clean, analyze, and interpret data using advanced tools and techniques. The primary objective is to empower learners to uncover insights, develop data-driven strategies, and present actionable recommendations that enhance organizational decision-making and performance.
What You Will Learn
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Our curriculum offers practical knowledge and hands-on experience to master the core aspects of data analysis. Here's what you will learn throughout the course:
Data Fundamentals
Data Types and Structures
Data Cleaning and Preprocessing
Handling Missing Data
Exploratory Data Analysis (EDA)
Data Visualization
Tableau
Power BI
Python Libraries (Matplotlib, Seaborn)
Excel Dashboards
Programming for Data Analysis
Python (Pandas, NumPy)
R Programming
SQL Queries for Data Retrieval
Bash Scripting
Statistical Analysis
Descriptive and Inferential Statistics
Hypothesis Testing
Probability Distributions
Regression Analysis
Data Handling Tools
Microsoft Excel (Pivot Tables, VBA)
Google Sheets
BigQuery
ETL Processes
Database Management
Relational Databases (MySQL, PostgreSQL)
NoSQL Databases (MongoDB)
Data Warehousing Concepts
Query Optimization
Business Intelligence
KPI Metrics
Building Reports and Dashboards
Real-Time Analytics
Storytelling with Data
Machine Learning Basics
Introduction to ML Algorithms
Clustering and Classification
Predictive Analytics
Feature Engineering
Tools for Big Data
Hadoop Ecosystem
Apache Spark
Data Pipelines
Cloud Data Platforms (AWS, Google Cloud, Azure)
Professional Development
Building a Data Portfolio
Certifications (Google Data Analytics, Tableau, SQL)
Data Analyst Career Roadmap
Networking and Industry Insights
Emerging Trends
Data Ethics and Privacy
Natural Language Processing (NLP)
Real-Time Data Processing
AI in Data Analytics
