Data Analyst is called the job of the future. More and more companies are opening jobs for data analysts.
Why? What is data?
Let’s take it in a simple way: ask yourself how many sales people do you employ to find 100 new clients? Do you know that one data scientist can give you those 100 clients that you didn’t know that you already have?
We, as companies, produce and gather data all the time, but 95% of companies don’t know how to use the data they already possess.
Investing in an employee to be part of the Data Science Academy is one of the best decisions you can make.
Investing in yourself to develop a career in Data Science is a choice toward a well-paid career.
Becoming a Data Analyst will also help you:
- Quantify all the collected data
- Provide suggestions for a company
- Identify good opportunities for the company
- Perform experiments
- Be knowledgeable about target audiences
- Connect brands with customers
- Forecast for the future based on real data
- Improve existing processes and systems
Switching careers has never been so easy!
It doesn’t matter which field you come from or what age you are, if you are passionate about it and you focus your time and attention, you will get the right skills to start a career in data science. It is easier when you learn from experts on the job. We have gathered top professionals to guide you and share their knowledge with you.
Interactive online program or in person
The program is available both online and in person. For the interactive online program, students get a link to join prior to every class. The benefit of the online program is that it is available wherever you are. If you missed a class, don’t worry, as you will also receive a video recording. These video recordings in the online program also help you go back and re-watch the same class again.
Through the years we have had multiple cases with students where sometime they have too much work and therefore miss some of the classes. Don’t worry. Come attend the Academy again. We will give you the opportunity to take the Academy one more time for free. Here at Creative Hub, we vow to you that you will learn!
There will never be a student who can say, “I didn’t learn here.” This is our promise to you!
What will my job title be?
Data Analyst or Junior Data Scientist.
Partnership network & events
Our goal at Creative Hub is not only to give you the skills from our experienced lecturers but also to connect you with peers in the Data Science industry.
We are a community of Data Scientists, and we connect you with the world.
Introduction to Data Science
- Basic concepts & terminology
- Online webinars
- Overview of data science
- Guide to statistics
- Guide to calculus
- Collection of articles
- SQL exercises
- Workshop 1 – Advanced Excel 1
- Workshop 2 – Advanced Excel 2
- SQL environment setup
- SQL data types
- Basics of SQL query statements
- Querying data
- Data grouping and computing aggregates
- Table joins
- Window functions in SQL
- Rollups and cubes
- Data warehouse concepts and architectures
- Multidimensional data – representation and manipulation
- Data warehouse design methodologies
- Data integration concepts
- Architectures, features, and details of data integration tools
- Get started with Power BI
- Connect to external data, clean, and transform
- Data relationships and calculations
- Explore your time-based data
- Data visualizations
- R integration in Power BI Desktop
- Create and configure a dashboard
- Create custom Q&A suggestions
- Introduction & calculation of DAX
- Descriptive statistics fundamentals
- Inferential statistics fundamentals
- Confidence intervals
- Hypothesis testing
- Simple linear regression
- Diagnostics and remedial measures
- Multiple linear regression
- Modeling and model selection
- Logistic regression model
- Linear mixed model
- Poisson regression model
- Ordered logic models
- Integration data with Excel, SQL
- Creating Data Warehouse cube
- Loading data from database into Power BI
- Using statistics and DAX formulas
- Integration of SQL, Data Warehouse into Python
- Intro to Python
- Basic syntax, data types, variable declarations, conditions, loops, strings
- Data set in Python
- Working with lists, tuples, and ranges
- Dictionaries and sets
- Functions, modules, and object-oriented programming in Python
- Working with modules, the import keyword
- Functions, function definitions, function scope, recursion
- Classes, class instances, objects, constructors
- Class attributes and methods, access modifiers
- Getters, setters, and properties
- Intro to machine learning
- Environment setup for machine learning in Python
- Classification algorithms
- Choosing a classification algorithm
- Logistic regression
- Support vector machines
- Decision tree learning
- K-nearest neighbor
- Data preprocessing.
- Data compression with dimensionality reduction
- Model evaluation and hyperparameter tuning
- Handling missing data
- Handling categorical data and properties
- Partitioning a dataset into train and test data
- Selecting meaningful features
- Assessing feature importance with random forests
- K-fold cross-validation
- Grid search and validation curves
- Performance evaluation metrics
- Predicting continuous target variables with regression analysis
- Linear regression
- Polynomial regression
- Working with unlabeled data.
- Unsupervised learning
- Introduction to big data
- Characteristics of big data & scalability
- Distributed big data systems
- Relational databases vs. distributed file systems
- Getting started with Hadoop
- Hadoop Distributed File System (HDFS)
- HDFS architecture
- HDFS tuning parameters
- HDFS performance and robustness
- Map reduce
- Tuning a mapper, tuning a reducer
- Using cache for lookups
- Workshop Week
- Final project
In Albanian Language
• Betim Biba in
IT Business Analyst at Raiffeisen Bank Vienna. Data Warehouse and Group Solutions Senior Specialist with 5+ years in banking industry with experience in IT including data migration, data integration, data transformation, data warehousing, and IT project management.
• Lirim Zenuni in
Head of MIS at Meridian Express with 8 years of experience in Data Analytics.
Throughout his experience, he has specialized in utilizing Business Intelligence tools to develop considerate answers for different business questions and opportunities.
As of 2015, Lirim has developed Power BI reports for Meridian’s chain of 39 stores, which has increased efficiency and credibility in reporting. He is also a Business Intelligence consultant at PECB and Analytics, where he has also implemented Power BI as a Business Intelligence tool.
• Hana Hoxha in
Around 15 years of professional work experience with a wide range of expertise in private sector, especially in market intelligence, monitoring & analytics, product development, and marketing campaigns. Apart from the private sector experience, she has been engaged in private sector development projects- Market Systems Development Approach (MSD), government and academic institutions.
• Afrim Rexhepi
Afrim Rexhepi is a master of computer science and Engineering. He is working on the position of Data Engineer at Raiffeisen Bank.
With over 15 + years of experience in processing, management, and reporting on data (Date), currently leading the innovation for Big Data Technologies in Cloud (AWS – Amazon Web Services) for data storage and processing for the working company, using platforms from AWS such as: S3, EC2, Amazon EMR (Hadoop), Athena, Apache Airflow, Apache Spark, etc.
• Agon Cecelia in
Agon Cecelia has a long experience as developer in different companies. He wrote his first lines of code when he was 13 and built websites, blogs, and maintained forums during high school. Agon now has co-founded NeoX LLC, and apart from being the CEO, he is also a Software Architect there.
• Gentrit Rexhepi in
Gentrit Rexhepi is a data integration specialist with 4 + years of experience and computer engineering education. Currently works as a business analyst and data integration specialist at Data Warehouse under Raiffeisen Bank.
During his work, Gentriti is responsible for creating ETL logics (extract, transform, load) for data integration between different systems, model development and maintenance of warehouse dates, data quality, optimization of performance long integration and plaguing of integration capacities, thus ensuring fulfillment of business requirements.
The next group is starting in 15th of May
7 Months (152 lecturing hours)
Pay on 15,24,36 or 50 installments
Online Preparation Period
Assistance with Job Placement after the Academy
International Certificate + Diploma Supplement with ECTS Credits