What will i learn?
Curriculum for this course
115 Lessons
00:00:00 Hours
Data Gathering
15 Lessons
00:00:00 Hours
- Overview 00:00:00
- Gathering webdata from curl 00:00:00
- Extracting Spreadsheet Data with in2csv 00:00:00
- Extracting Spreadsheet Data with Agate 00:00:00
- Extracting Legacy Data from dBASE Tables 00:00:00
- Extracting HTML Data in Python 00:00:00
- Gathering Metadata 00:00:00
- Working with HTTP Headers 00:00:00
- Working with Linux Log Files 00:00:00
- Working with Email Headers 00:00:00
- Connecting to Remote Data 00:00:00
- Copying Remote Data 00:00:00
- Synchronizing Remote Data 00:00:00
- Exercise Explore HTML Tables 00:00:00
- Data Gathering
Data Filtering
12 Lessons
00:00:00 Hours
- Data filtering techniques & tools 00:00:00
- Processing Data Formats 00:00:00
- Filtering HTTP headers 00:00:00
- Filtering CSV data 00:00:00
- Replacing Values with sed 00:00:00
- Dropping Duplicate Data 00:00:00
- Working with JPEG Headers 00:00:00
- Filtering PDF files 00:00:00
- Filtering for invalid data 00:00:00
- Parsing robots.txt 00:00:00
- Practical exercise_Cull old data 00:00:00
- Quiz
Data Transformation
11 Lessons
00:00:00 Hours
- Converting CSV to JSON 00:00:00
- Converting XML to JSON 00:00:00
- Converting SQL to CSV 00:00:00
- Converting SQL to CSV 00:00:00
- Changing CSV delimiters 00:00:00
- Converting Dates 00:00:00
- Converting numbers 00:00:00
- Rounding numbers 00:00:00
- OCR JPEG Images 00:00:00
- Extracting Text from PDF files 00:00:00
- Exercise_Convert dates to ISO 8601 00:00:00
Data Exploration
12 Lessons
00:00:00 Hours
- Exploring CSV data 00:00:00
- Exploring CSV Statistics 00:00:00
- Querying CSV Data 00:00:00
- Plotting from the command line 00:00:00
- Counting words 00:00:00
- Exploring directory trees 00:00:00
- Determining word frequencies 00:00:00
- Taking random samples 00:00:00
- finding the top rows 00:00:00
- Finding repeated records 00:00:00
- Identifying outliers in data 00:00:00
- Exercise_Count word frequencies in a classic book 00:00:00
Data Classification & Machine Learning
18 Lessons
00:00:00 Hours
- Introduction to supervised learning 00:00:00
- Introduction to unsupervised learning 00:00:00
- Understanding linear regression 00:00:00
- Working with predictors 00:00:00
- understanding logistic regression 00:00:00
- understanding dummy variables 00:00:00
- using naive bayes classification 00:00:00
- working with decision trees 00:00:00
- K-means Clustering 00:00:00
- using cluster validation 00:00:00
- using principal component analysis 00:00:00
- Introduction to errors 00:00:00
- defining underfitting 00:00:00
- defining overfitting 00:00:00
- using k-folds cross validation 00:00:00
- Using neural networks 00:00:00
- support vector machine(SVM) 00:00:00
- exercise_choose a machine learning method 00:00:00
Data Communication & Visualization
15 Lessons
00:00:00 Hours
- Effective Communication & Visualisation 00:00:00
- Correlation vs Causation 00:00:00
- Simpson Paradox 00:00:00
- Presenting Data 00:00:00
- Documenting data science 00:00:00
- Visual Data Exploration 00:00:00
- Creating Scatter Plots 00:00:00
- Plotting line graphs 00:00:00
- Creating bar charts 00:00:00
- Creating histograms 00:00:00
- Creating box plots 00:00:00
- Creating Network visualizations 00:00:00
- Creating bubble plot 00:00:00
- Creating interactive plots 00:00:00
- Practical Exercise_Create a scatter plot 00:00:00
Data Integration
10 Lessons
00:00:00 Hours
- Joining CSV data 00:00:00
- Concatenating log files 00:00:00
- Sorting text files 00:00:00
- merging XML data 00:00:00
- Aggregating data 00:00:00
- normalizing data 00:00:00
- denormalizing data 00:00:00
- pivoting data tables 00:00:00
- homogenizing rows 00:00:00
- Practical exercise_merge to CSV documents into one 00:00:00
Data Analysis Concepts
19 Lessons
00:00:00 Hours
- Basic Data Science math 00:00:00
- Linear algebra vector math 00:00:00
- Linear Algebra matrix math 00:00:00
- Linear Algebra Matrix Decomposition 00:00:00
- Data Formation 00:00:00
- Introduction to Probability 00:00:00
- Working with Events 00:00:00
- Working with Probability 00:00:00
- Continuous probability distributions 00:00:00
- Discrete probability distributions 00:00:00
- Introduction to bayes theorem 00:00:00
- Sampling data 00:00:00
- Statistical measures 00:00:00
- Estimators 00:00:00
- Sampling distributions 00:00:00
- Confidence Intervals 00:00:00
- Hypothesis Tests 00:00:00
- Chi square Test 00:00:00
- Exercise_Identifying data sets by type 00:00:00
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