My Skillset includes:
1) Data Preprocessing:
Data preprocessing is an essential step in any data analysis project. It involves cleaning and wrangling the data to make it more consistent and accurate. This includes dealing with inconsistent data, missing values, duplicate values, and other problems that may exist in the dataset. By preprocessing the data, we can ensure that our results are more reliable and trustworthy. Preprocessing also helps us identify any potential biases or anomalies in the dataset which can be addressed before further analysis is conducted.
2) Exploratory Data Analysis:
Exploratory Data Analysis (EDA) analyzes data to summarize its main characteristics, often with visual methods. This type of analysis is used to gain insights about the data and uncover patterns in the data. It involves exploring the data through various means such as examining the data types, looking for correlations between variables, and generating visualizations that can help identify any data trends or anomalies in the dataset. By understanding the different aspects of a dataset, EDA can provide valuable insights that can be used to make decisions or drive further research.
3) Model Development:
Model development is an important part of data science. It helps to create predictive models that can be used for various tasks such as predicting future trends, classifying data, and analyzing complex datasets.
Model development involves the use of various algorithms and techniques such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. Using these algorithms and techniques makes it possible to develop accurate and reliable models that can be used for various purposes, including making predictions about the future or understanding complex datasets better.
4) Model Evaluation:
Model evaluation is an important step in the process of building machine learning models. It helps to determine how well a model performs and if it needs improvement. It also helps us understand how our model performs on unseen data and if it can be used for real-world applications. There are various techniques used for model evaluation such as cross-validation, holdout set, and A/B testing.
Tools That I Use:
1) Python
2) SQL (MySQL, BigQuery, Db2)
3) Spreadsheets
4) Visualizing Tools (Python and Tableau)
