What will i learn?
Curriculum for this course
86 Lessons
00:00:00 Hours
Data Science Essentials
86 Lessons
00:00:00 Hours
- Data Gathering - 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 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
- 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
- Data Transformation - 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
- Data Exploration - 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
- Data Classification - Introduction to supervised learning 00:00:00
- Introduction to unsupervised learning 00:00:00
- Working with predictors 00:00:00
- Understanding linear regression 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
- Data Communication - 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
- 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
- Data Integration - 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
Data Science using Python
0 Lessons
00:00:00 Hours
Statistics for Data Science
0 Lessons
00:00:00 Hours
R for Data Science
0 Lessons
00:00:00 Hours
Data Science using R
0 Lessons
00:00:00 Hours
Machine Learning
0 Lessons
00:00:00 Hours
Natural Language Processing
0 Lessons
00:00:00 Hours
AI & Deep Learning using Tensorflow
0 Lessons
00:00:00 Hours
Big Data Hadoop
0 Lessons
00:00:00 Hours
Spark & Scala Training
0 Lessons
00:00:00 Hours
Tableau Training
0 Lessons
00:00:00 Hours
Requirements
+ View more
Description
+ View more
Other related courses
About the instructor
- 0 Reviews
- 785 Students
- 152 Courses
Student feedback
Reviews
Write a public review