Become a Data Analyst from Udacity

Udacity

Categories: Data Analytics, Data Science Tags: Intermediate, Micro-credentials

Background

Before knowing ‘how to become a Data Analyst’, let us understand what is data analysis? Data analysis involves manipulating, transforming, and visualizing data in order to infer meaningful insights from the results. Individuals, businesses, and even governments often take direction based on these insights.

Data analysts might predict customer behavior, stock prices, or insurance claims by using basic linear regression. They might create homogeneous clusters using classification and regression trees (CART), or they might gain some impact insight by using graphs to visualize a financial technology company’s portfolio. Until the final decades of the 20th century, human analysts were irreplaceable when it came to finding patterns in data. They are still essential when it comes to feeding the right kind of data to learning algorithms and inferring meaning from the algorithmic output, but machines can and do perform much of the analytical work itself.

Who is Data Analyst?

A data analyst is someone who collects, processes, and performs statistical analyses of data. A data analyst can translate numbers and data into plain English in order to help organizations and companies understand how to make better business decisions.

Moreover, a data analyst is an individual who is responsible to gather, investigate, and represent data and filter out useful information from it. Data analysts may have the following responsibilities:

• Working with technology teams, management, and data scientists to set goals.
• Mining data from primary and secondary sources.
• Cleaning and dissecting data to get rid of irrelevant information.
• Analyzing and interpreting results using statistical tools and techniques.
• Pinpointing trends and patterns in data sets.
• Identifying new opportunities for process improvement.
• Providing data reports for management.
• Designing, creating, and maintaining databases and data systems.
• Fixing code problems and data-related issues.

About this course

The program on how to become a data analyst prepares you for a career as a data analyst by helping you learn to organize data, uncover patterns and insights, draw meaningful conclusions, and clearly communicate critical findings. You will develop proficiency in Python and its data analysis libraries (Numpy, pandas, Matplotlib) and SQL as you build a portfolio of projects to showcase in your job search. Depending on how quickly you work through the material, the amount of time required is variable. We have included an hourly estimation for each section of the program. In order to succeed in this program, we recommend having experience working with data in Python (NumPy and Pandas) and SQL).

What you will learn from this course?

Advance your programming skills and refine your ability to work with messy, complex datasets. You’ll learn to manipulate and prepare data for analysis, and create visualizations for data exploration. Finally, you’ll learn to use your data skills to tell a story with data.

Why should you enroll in this course?

The Data Analyst Nano-degree program offers you the opportunity to master data skills that are in demand by top employers, such as Python and statistics. By the end of the program, you will have created a portfolio of work demonstrating your ability to solve complex data problems. After graduating, you will have the skills needed to join a large corporation or a small firm or even go independent as a freelance data analyst.

Syllabus on ‘Become a Data Analyst’ course

Course 1: Become a Data Analyst: Introduction

LESSON ONE: Anaconda

• Learn to use Anaconda to manage packages and environments for use with Python.

LESSON TWO: Jupyter Notebooks

• Learn to use this open-source web application to combine explanatory text, math equations, code, and visualizations in one sharable document.

LESSON THREE: Data Analysis Process

• Learn about the keys steps of the data analysis process.
• Investigate multiple datasets using Python and Pandas.

LESSON FOUR: Pandas and AND NumPy: Case Study 1

• Perform the entire data analysis process on a dataset.
• Learn to use NumPy and Pandas to wrangle, explore, analyze, and visualize data.

LESSON SIX: Programming Workflow for Data Analysis

• Learn about how to carry out analysis outside Jupyter notebook using IPython or the command line interface.

Course 2: Become a Data Analyst: Practical Statistics

LESSON ONE: Simpson’s Paradox

• Examine a case study to learn about Simpson’s Paradox.

LESSON TWO: Probability

• Learn the fundamental rules of probability.

LESSON THREE: Binomial Distribution

• Learn about binomial distribution where each observation represents one of two outcomes.
• Derive the probability of a binomial distribution.

LESSON FOUR: Conditional Probability

• Learn about conditional probability, i.e., when events are not independent.

LESSON FIVE: Bayes Rule

• Build on conditional probability principles to understand the Bayes rule.
• Derive the Bayes theorem.

LESSON SIX: Standardizing

• Convert distributions into the standard normal distribution using the Z-score.
• Compute proportions using standardized distributions.

LESSON SEVEN: Sampling Distributions and Central Limit Theorem

• Use normal distributions to compute probabilities.
• Use the Z-table to look up the proportions of observations above, below, or in-between values.

LESSON EIGHT: Confidence Intervals

• Estimate population parameters from sample statistics using confidence intervals.

LESSON NINE: Hypothesis Testing

• Use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.

LESSON TEN: T-Tests and A/B Tests

• Test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes.

LESSON ELEVEN: Regression

• Build a linear regression model to understand the relationship between independent and dependent variables.
• Use linear regression results to make a prediction.

LESSON TWELVE: Multiple Linear Regression

• Use multiple linear regression results to interpret coefficients for several predictors.

LESSON THIRTEEN: Logistic Regression

• Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors.

Course 3: Become a Data Analyst: Data Wrangling

LESSON ONE: Intro to Data Wrangling

• Identify each step of the data wrangling process (gathering, assessing, and cleaning).
• Wrangle a CSV file downloaded from Kaggle using fundamental gathering, assessing, and cleaning code.

LESSON TWO: 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.

LESSON THREE: 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.

LESSON FOUR: 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.

Course 4: Become a Data Analyst: Data Visualization with Python

LESSON ONE: Data Visualization in Data Analysis

• Understand why visualization is important in the practice of data analysis.
• Know what distinguishes exploratory analysis from Explanatory analysis, and the role of data visualization in each.

LESSON TWO: Design of Visualizations

• Interpret features in terms of the level of measurement.
• Know different encodings that can be used to depict data in visualizations.
• Understand various pitfalls that can affect the effectiveness and truthfulness of visualizations.

LESSON THREE: Univariate Exploration of Data

• Using bar charts to depict distributions of categorical variables.
• Histograms to depict distributions of numeric variables.
• Use axis limits and different scales to change how your data is interpreted.

LESSON FOUR: Bivariate Exploration of Data

• Using scatterplots to depict relationships between numeric variables.
• Clustered bar charts to depict relationships between categorical variables.
• Violin and bar charts to depict relationships between categorical and numeric variables.
• Use faceting to create plots across different subsets of the data.

LESSON FIVE: Multivariate Exploration of Data

• To use encodings like size, shape, and color to encode values of the third variable in a visualization.
• Use plot matrices to explore relationships between multiple variables at the same time.
• Using feature engineering to capture relationships between variables.

LESSON SIX: Explanatory Visualizations

• Understand what it means to tell a compelling story with data.
• Choose the best plot type, encodings, and annotations to polish your plots.
• Create a slide deck using a Jupyter Notebook to convey your findings.

LESSON SEVEN: Visualization Case Study

• Apply your knowledge of data visualization to a dataset involving the characteristics of diamonds and their prices.

This Nano-degree Program Includes:

• Experienced Project reviews.
• Technical mentor support.
• Personal career services.

How is the Nano-degree program structured?

Become a Data Analyst Nano-degree program is comprised of content and curriculum to support five (5) projects. They also estimated that students can complete the program in four (4) months working 10 hours per week. Each project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

About Project description

Throughout this Nano-degree program, you will have the opportunity to prove your skills by building the projects.

Prerequisites:

In order to succeed in this program, Udacity recommends you to have some working experience with data in Python (specifically NumPy and Pandas) and SQL. Which includes:

• Python standard libraries.
• Working with data with Pandas and NumPy.

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