This repository is mainly for projects I have done under the influence of :
- 'Self learning activities' by searching projects and implementing datasets after which i perform data analysis procecss.
- Under Skillslash Academy's accalaimed course - Data Science with Artificial Intelligence & Machine Learning which spans for a period of 12 months.
- Under Udacity's Data Analysis Nanodegree course syllabus roof.
Skillslash Academy & Udacity's online Data Science and Data analyst program prepares me for a career as a industry ready data analyst by helping me learn to clean and organize data, uncover patterns and insights, draw meaningful conclusions, and clearly communicate critical findings. I am developing proficiency in Python and its data analysis libraries (Numpy, pandas, Matplotlib) and SQL as I build a portfolio of projects .
Subjects Covered in these portfolio of projects :
- Anaconda
- Jupyter Notebook
- Data Analysis Process
- NumPy for 1 and 2D Data
- Pandas Series and Dataframes
In this project, I choose one of Udacity's curated datasets and investigate it using NumPy and pandas. I complete the entire data analysis process, starting by posing a question and finishing by sharing the findings. ( It may be better to place this section inside the readme of the project 1)
I was provided a dataset reflecting data collected from an experiment. I used statistical techniques to answer questions about the data and report my conclusions and recommendations in a report.
Subjects Covered:
- Probability
- Conditional Probability
- Binominal Distribution
- Sampling Distribution and Central Limit Theorem
- Descriptive Statistics
- Inferential Statistics
- Confidence Levels and Intervals
- Hypothesis Testing
- T-tests and A/B test
- Regression
- Multiple Linear Regression
- Logistic Regression
#Project 3: Analyze A/B Test Results with company ab_data.csv
Using Python, I gathered data from a variety of sources, assess its quality and tidiness, then cleaned it. I documented the wrangling efforts in a Jupyter Notebook, plus showcased them through analyses and visualizations using Python and SQL.By using AB Testing and regression methods to decide if the company should launch a new webpage or keep the old one.
Subjects Covered:
GATHERING DATA:
- Gather data from multiple sources, including gathering files, programmatically downloading files, web-scraping data, and accessing data from APIs
- Import data of various file formats into pandas, including flat files (e.g. TSV), HTML files, TXT files, and JSON files
- Store gathered data in a PostgreSQL database
ASSESSING DATA
- Assess data visually and programmatically using pandas
- Distinguish between dirty data (content or “quality” issues) and messy data (structural or “tidiness” issues)
- Identify data quality issues and categorize them using metrics: validity, accuracy, completeness, consistency, and uniformity
CLEANING DATA
- Identify each step of the data cleaning process (defining, coding,and testing)
- Clean data using Python and pandas
- Test cleaning code visually and programmatically using Python
#Project 4 : Data Wrangle and Analyze with Tweet WeRateDogs data
Collect data from different sources and assess data visually and programmatically , clean data for visulizing data and finding insights later.
Subjects Covered:
- Univariate exploration of data ( histogram , bar charts , Use axis limits and different scales )
- Bivariate exploration of data ( scatter plots , clustered bar charts , violin and bar charts , faceting )
- Multivariate exploration of data ( encodings , plot matrices , feature enginnering )
- Explanatory Visulizations ( story telling with data , polish plots , create slide deck )
Data visualization to a dataset involving the characteristics of diamonds and their prices.
In this project, I used Python’s data visualization tools to systematically explore the bike dataset for its properties and relationships between variables. Then, I created a presentation that communicates the findings to others.