Salary Management Project in SQL
Salary management project, from creating a database to creating the final DWH and loading it with all necessary data.
Enthusiastic Data Analyst, experienced in financial/business analysis, data science, risk management. Skilled in SQL, Python, Power BI. @dragana-pahovec-ab13a457.
Salary management project, from creating a database to creating the final DWH and loading it with all necessary data.
Data preprocessing, EDA (in Python) include relevant dataset for 197 countries for 200 years time horizon.
This is a countrywide car accident dataset, which covers 49 states of the United States. The data is collected from February 2016 to December 2019, using several data providers.
Design database, then DataWareHouse for the database. The main purpose of the DW is to keep the accounts balance on monthly level.
Full Initial Load using functions and stored procedures in SQL Server.
Incremental load in SQL Server using functions and stored procedures in SQL.
Extend the ETL workflow, from WorldWideWeb to WorldWideWeb DataWarehouse, with the help of newly created stored procedure which will synchronize the given dimension.
Regression learning data preprocessing with algorithms, using the given dataset. The goal is to predict the PM10 concentration using the weather-meteorological data.
A classification problem, using prediction and decide wheather or not the credit should be approved for a client.
Diabetes metrics and evaluations using different classifiers, where the accuracy has to be compared using each of it.
EDA, evaluation, metrics. Given dataset, all features explained in the link below.
Regression learning, data preprocessing, algorithms feature selection, hyperparameter optimization. The goal is to predict the PM10 concentration using the weather-meteorological data.
EDA, evaluation, metrics. Predicting whether a customer will change telecommunications provider, something known as "churning".
EDA, General analysis.
Some useful python functions and solutions.
Training the model using the unsupervised learning.
Training models and basic preprocessing in Scikit-Learn. If successful, the model has to identify applicants that are at high risk to default, allowing the bank to refuse credit requests.
Pyplot, Pyfile, PlyData
Calculus in Python.
Descriptive Statistics & Intro to Probability in Python.
Hypothesis Tests in Python.
Linear algebra in Python.
Multivariable Calculus in Python.
Probability distribution in Python.
Regression Analysis in Python.
Project prepared for an NGO client, using their survey data (chaotic excel file that had to be cleaned first) and prepared Power BI dashboard.
Adventure Works Report, cleaned with Power Query and created Power BI dashboard.
Picture classification Spark ML.
Spark RDD.
Spark SQL.
Callao Salvaje 38
Adeje, Tenerife, Spain 38678