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This repository is mainly for projects I have done under Udacity-Data-Analysis-Nanodegree.

Udacity online data analyst program prepares me for a career as a 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 .

Tips: For data science projects with python, I would recomend you to install numpy , pandas , scipy , scikit learn , matplotlib , seaborn thest basic libraries.

Subjects Covered:

• Anaconda: Learn to use Anaconda to manage packages and environments for use with Python
• Jupyter Notebook: Learn to use this open-source web application
• 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

Using Python, I gathered data from a variety of sources, assess its quality and tidiness, then clean it. I documented the wrangling efforts in a Jupyter Notebook, plus showcase 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

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 forits properties and relationships between variables. Then, I created a presentation that communicates the findings to others.