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Day 1
- It's all about the introductions and intro to the challenge
- A basic data test
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Day 2
- Today's learnings were on data lifecycle, data vocabulary, types of data, types of analytics.
- Data lifecycle: It ranges from data collection to deployment. Data collection, data storage, data cleaning, data exploration, data visualisation, data modelling, data communication and deployment.
- Data Vocabulary: It discussed different words related to the data world. Data Analysis, Data Science, Data Engineering, Machine Learning, Deep Learning, AI, Big Data, Cloud Computing.
Types of data: Qualitative (Nominal, Ordinal) and Quantitative (Discrete, Continuous)
Types of analytics: Descriptive, Diagnostic, Predictive, Perspective.1. Descriptive: What happened? 2. Diagnostic: Why did it happen? 3. Predictive: What will happen? What might happen? 4. Perspective: What can be done to achieve? Further steps -
Day 3
- Today its all about the different data careers available. Data Analyst, Data Engineer and Data Scientist.
- Path to transition to these careers.
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Day 4
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It's a discussion on data cleaning. There are six types of dirty data.
- Misentries - > Spellings, typos, inconsistent data logging
- Duplicates -> No row is repeated
- Missing Data -> NaN's empty data
- Combining Data Sources -> Combining two or more sources of data
- Unwanted outliers -> Deciding upon what to do with the outliers
- Quality check -> Data Quality Check
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Open Refine is the tool which I got to know for data cleaning.
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All the data cleaning steps can be found here
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Day 5
- Descriptive Statistics gives insights about the data. Different columns have been analysed.
- In general, with respect to descriptive statistics following are depicted
- Aggregate / Summary statistics
- Proportions
- Counts
- Min/Max
- Distributions
- Different factors with respect to number of crimes, gender and race have been analysed.
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Day 6
- It's a discussion on Data Visualization, importance of data visualization
- Different types of charts
- Different data viz tools
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Day 7
- Graph Representation
- Flourish
- Pivot Table creation
- Racing bar chart with number of crimes
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Day 8
- Tableau Introduction
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Day 9
- Pie Chart
- Dashboard.
- Here's the link to dashboard. It keeps on updating as I proceed further in the challenge.
- Difference between a bad pie chart and a good pie chart.
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Day 10
- Data Exploration and visualization using the data.
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Day 11
- Today's learning is about creating a map using Tableau with longitude and latitude data
- Along with that creating a dashboard in Tableau.
- Here's the link to dashboard. It keeps on updating as I proceed further in the challenge.
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Day 12
- Today's learning is on Presenting and Delivering insights.
- Different types of delivering presentations - Tableau file, report, export, stand alone graphs, ppt, demo, video
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Day 13
- Kind of a holiday to work with pending tasks
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Day 14
- Introduction to SQL
- Discussed on all the basic concepts of SQL (LIMIT, IN, Aggregate functions, Joins, group by, Order by)
- Link to website that convert CSV data to SQL table. Link
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Day 15
- Data Exploration using SQL queries
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Day 16
- Introduction to Python
- Why Python for Data Science, Advantages of Python
- Important Libraries (Pandas, Numpy, matplotlib and Seaborn)
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Day 17
- Today's discussion was on Data Wrangling
- Different functions of Pandas (describe, info, loc, iloc)
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Day 18
- Data visualization using Python
- Different Libraries (matplotlib, seaborn, plotly, bokeh, dash, seaborn, altair, folium)
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Day 19
- Introduction to Machine Learning
- Types of ML (Supervised, Unsupervised)
- Grouping, Quantification, Forecasting way of addressing ML problems.
- Brief intro to Linear Regression
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Day 20
- Today's discussion is on Classification or Grouping
- Classification on Iris data
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Day 21
- Wrap up and complete overview
- Data Quiz
- Feedback