Skip to content

Latest commit

 

History

History
89 lines (61 loc) · 4.38 KB

README.md

File metadata and controls

89 lines (61 loc) · 4.38 KB
Raw

Data-Analytics-Projects:

Certificate : https://graduation.udacity.com/confirm/KUM3F4AJ

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.

Part 1 - Intro to Data Analysis

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

Project 1: Explore Weather Trends with weather forecast data

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)

Project 2: Investigate a dataset called TMDb movie data.

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.

Part 2 -Practical Statistics

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 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.

Part 3 - Data Extraction and Wrangling

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.

Part 4 - Data Visualisation

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 )

Project 5: Data Visulization with Diamond Data

Data visualization to a dataset involving the characteristics of diamonds and their prices.

Project 6: Communicate data finding with Ford Go Bike Sharing Data

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.