Claudia Gold: Learning Data Science Online

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Claudia Gold, author of the Data Analysis Learning path on SlideRule, talks about why she wrote it and how to approach learning data science on your own. https://www.mysliderule.com/learning-paths/data-analysis/

Data & Analytics

How to teach yourself data science
by Claudia Gold

SlideRule learning path:
The minimum you need
to be an entry-level data scientist

What do I need to know
1to be a data scientist?

Statistics
Areas of Data Science
Modeling
and
forecasting Machine
Learning
Data
Visualization
Data Engineering

General computer knowledge:
Version control, the command
line, cron, etc

Designing sound experiments:
Research methodology

Business Sense
• Ask the right questions
• Focus on actionable insights
•Gain subject expertise

Math & Statistics
Knowledge
Source: Adapted from
Data Science Venn Diagram,
author Drew Conway,
Machine
Learning
Data
Science
Traditional
Research
posted to Wikimedia on Sep 30, 2010.
Hacking Skills
Danger
Zone!
Subject
Expertise

Learning data science takes months,
not hours.
(and that’s assuming you already know some math)

How these tools might fit together
Website data
SQL
database
Customer
support data
Event
tracking data
R or
Python
Website
Predictive
models
Dashboards
and reports
Analysis

What is it really like
to 3 be a data scientist?

Source: Programmer writing code with
unit tests, author Joonspoon, uploaded
to Wikipedia on November 4, 2014

Working with other teams
Data
Science
Marketing
Product
Business
decisions
Data
products
Operations

How you’ll spend your time
80%
20%
Analyzing data
Preparing to analyze data

Accuracy and completeness
vs
time constraints

Communicating results
to varied audiences

Advocating for data
within an organization

Unlimited applications
Source: Burtch Works Study, Salaries of Data Scientists, pg 14

Play a vital role within an organization

What's hot

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Much of human knowledge is “locked up” in a type of data called text. Humans are great at reading, but are computers? This workshop leads you through open source data science libraries in Python that turn text into valuable data, then tours an open source system built for the Wordnik dictionary to source definitions of words from across the internet. Thinking Machines Conference, Manila, February 2016 http://thinkingmachin.es/events/

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Understanding Data Science: Unveiling the Basics What is Data Science? Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems. Importance of Data Science In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation. Intersection of Data Science, Statistics, and Computer Science Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently. Key Components of Data Science Data Collection and Storage The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing. Data Cleaning and Preprocessing Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format. Exploratory Data Analysis (EDA) EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis. Machine Learning and Predictive Modeling Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data. Data Visualization Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively. The Data Science Process Problem Definition The data science process begins with understanding the problem you want to solve and defining clear objectives. Data Collection and Understanding Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data. Data Preparation Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling. Model Building Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models. Model Evaluation and Deployment Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data. Technologies Driving Data Science Programming Languages Languages like Python and R are widely used in data science due to their extensive libraries and versatility. Machine Learning Libraries Libraries like Scikit-Learn and TensorFlow prov

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