Data analyst bootcamps in Tbilisi for working professionals

Discover the best beginner data analyst bootcamps in Tbilisi tailored for working professionals. These intensive programs will teach you how to transform raw data into actionable insights using advanced techniques, helping organizations make informed decisions and excel in a data-driven world.

Key Highlights

Industry-Experienced Instructors

Comprehensive curriculum

Expert faculty with industry experience

Prestigious certification upon completion

Ongoing support and resources post-program

Live Online Training

Career advancement opportunities

Transformative learning experience

Flexible Learning Schedule

About the program

Data analytics involves examining, transforming, and interpreting large data sets to uncover patterns, extract insights, and aid decision-making. It starts with gathering and cleaning data from various sources to ensure accuracy. By exploring and visualizing the data, analysts gain insights into its characteristics and identify key trends. They then apply statistical methods, machine learning algorithms, and other analytical techniques to discover patterns and relationships. These insights are communicated to stakeholders to support data-driven decisions. Data analytics is essential across industries for optimizing processes, refining strategies, and gaining a competitive edge. It utilizes tools and technologies like programming languages, statistical software, data visualization tools, and advanced methods such as data mining and machine learning. Ultimately, data analytics helps organizations leverage data to drive innovation, make informed decisions, and achieve their business goals.

What Will You Learn?

Introduction to Data Analytics

You’ll learn the basics of data analytics, including its importance, process, and applications across industries.

Exploratory Data Analysis

You’ll learn to explore and visualize data using descriptive statistics and tools to identify patterns, trends, and relationships.

Data Collection & Preparation

You’ll learn to gather data from sources like databases and APIs, and use methods to ensure its quality and consistency.

Big Data Analytics

You’ll be introduced to big data technologies such as Apache Hadoop and Apache Spark for processing and analyzing large datasets.

Data Visualization

You’ll learn to use tools like Tableau, Power BI, and Python libraries to create clear, meaningful data visualizations.

Statistical Analysis

You’ll study statistical methods like hypothesis testing, regression, correlation, and ANOVA to extract insights and make data-driven decisions.

Transitioning to a new career

Studying AI and ML opened doors I never imagined. From creating predictive models for healthcare to analyzing market trends, these skills are in high demand. I secured a role at a tech startup right after graduation

Jessica M

AI Engineer

Transitioning from finance to AI and ML posed challenges, but the program’s thorough curriculum and hands-on projects boosted my confidence and skills. Now thriving as a machine learning engineer, I’m grateful for the career shift and the opportunities it brings.

Max Fischer

Machine Learning Engineer
 

Coming from a teaching background, I had reservations about transitioning into data science, but the program’s support and hands-on approach made it feasible. The job placement assurance was a significant motivator, and I am now thriving as a data analyst at a prestigious firm.

Emily Chen

Machine Learning Engineer
Transitioning to AI and ML was a dream come true for me, thanks to the AI/ML Certification Program. The hands-on projects and practical experience built my foundation. Now thriving as a machine learning engineer, I leverage this certification for exciting tech opportunities.

Jack Davies

Data Scientist

Professional Growth and Development

Increased Opportunities and Advancement

Alignment with Personal Goals and Values

Adaptability and Resilience:

Who Can Apply for the Course?

Roles That Data Analysts Can Fulfill

Data Analyst

Analyzes data to generate actionable insights and support decision-making.

Business Intelligence Analyst

Uses data to create reports and visualizations that inform strategic business decisions.

Data Scientist

Applies statistical and machine learning techniques to analyze complex data sets and predict trends.

Data Engineer

Designs and manages data pipelines and databases to ensure data quality and accessibility.

Data Visualization Specialist

Creates visual representations of data to effectively communicate findings and trends.

Analytics Consultant

Provides expertise in data analysis to solve business problems and optimize processes.

Data Analyst course Curriculum

  • Week 1-2: Fundamentals of Data Analysis
    • Introduction to Data Analysis
    • Data Types and Sources
    • The Data Analysis Process
  • Week 3-4: Tools and Technologies
    • Introduction to Data Analysis Tools (Excel, SQL)
    • Data Visualization Basics (Tableau, Power BI)
  • Week 1-2: Data Collection Techniques
    • Data Collection Methods
    • Data Sources and Databases
  • Week 3-4: Data Cleaning and Preparation
    • Data Cleaning Techniques
    • Handling Missing Values and Outliers
    • Data Transformation
  • Week 1-2: Descriptive Statistics
    • Measures of Central Tendency
    • Measures of Variability
  • Week 3-4: Inferential Statistics
    • Hypothesis Testing
    • Confidence Intervals
    • Regression Analysis
  • Week 1-2: Data Visualization Techniques
    • Types of Charts and Graphs
    • Creating Dashboards
  • Week 3-4: Reporting and Communication
    • Creating Reports
    • Effective Data Storytelling
  • Week 1-2: Advanced Excel Functions
    • Pivot Tables
    • Advanced Formulas
  • Week 3-4: Introduction to Python/R for Data Analysis
    • Basic Programming Concepts
    • Data Analysis Libraries (Pandas, NumPy for Python or dplyr, ggplot2 for R)
  • Week 1-2: Case Studies and Practical Applications
    • Industry-Specific Data Analysis
    • Real-World Problem Solving
  • Week 3-4: Capstone Project
    • Define and Scope the Project
    • Data Collection, Analysis, and Reporting
    • Presentation of Findings

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Skill Covered

Data Cleaning

SQL Proficiency

Descriptive Statistics

Python/R for Data Analysis

Inferential Statistics

Reporting Skills

Data Visualization

Critical Thinking and Problem-Solving

Tools Covered

Frequently Asked Questions

The duration varies from 2 months to 9 months, depending on the course you choose. Part-time and full-time options are available to fit your schedule.

Most courses offer flexible formats, including online, on-campus, and hybrid options, allowing you to choose the one that best suits your needs.

While some courses require basic knowledge of programming and statistics, others are designed for beginners. Check the prerequisites for each course before applying.

Yes, you will receive a certificate of completion or a degree, depending on the course you choose. These credentials can enhance your resume and career prospects.

Many course providers offer financing options, including installment plans, scholarships, and employer-sponsored programs. Check with the course provider for specific details.

Data science skills are in high demand across various industries. Completing these courses can open up new career opportunities, lead to higher salaries, and provide job security.

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