-Learn basic fundamentals of python (control structures, data types, syntax, functions, Object Oriented Programming and data structures).
-Build a core of Statistics and statistical models:(hypothesis testing, regression analysis, calculus, probability and linear algebra to help you in drawing insights and making informed decisions.
-Data collection/scraping(learn to collect data from relevant sources from various sources including databases, web scraping using(scrapy, Beautiful soup) and APIs.
-Data cleaning: Learn to clean data in order to get rid of anomalies this can be done using python libraries(numpy, pandas).Learn how to handle missing values and inconsistencies.
-Learn the fundamentals of SQL(Aggregation Functions, Joins, Nested queries,
CREATE TABLE,GROUP BY, ALTER, INSERT, DELETE, DROP TABLE, ORDER BY, UPDATE).Gain proficiency in database management and working with large datasets.
-Data visualization: (Visualize data through chart and bar graphs in order to understand trends, variations and drive meaningful insights from the data. Gain familiarity with tools like Tableau, Power BI, and python packages(matplotlib, Seaborn).
-Become proficient in Machine Learning: Be well versed with different machine learning models and when they are used based on the problem and data characteristics(Linear Regression, Naive Bayes, Reinforcement Learning, Random Forest, Decision Trees, Neural Networks).This helps in training models and achieve high accuracy and precision.
-Learn Git and version control for Augmented Analysis.Helps in collaborating with others in doing projects and tracks the changes you make and save them.
-Learn soft skills including communication. Learn how to story tell about data to both technical and non-technical audiences. Be able to summarize the findings in a clear and understandable manner and provide recommendations and insights based on the insights.
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