Requirements : Age 11+
Duration : 32 hours ( 4 Months )
What is this Course ?
Have you ever wondered how Amazon, eBay suggest items for you to buy?
How Gmail filters your emails in the spam and non-spam categories?
How Netflix predicts the shows of your liking?
How do they do it? These are the few questions we ponder from time to time.
In reality, doing such tasks are impossible without the availability of data. Data science is all about using data to solve problems. The problem could be decision making such as identifying which email is spam and which is not.
Or a product recommendation such as which movie to watch? Or predicting the outcome such as who will be the next President of the USA?
So, the core job of a data scientist is to understand the data, extract useful information out of it and apply this in solving the problems.
Why this Course ?
Why ML is so good today; for this, there are a couple of reasons like below but not limited to though.
1. The explosion of big data
2. Hunger for new business and revenue streams in this business shrinking times
3. Advancements in machine learning algorithms
4. Development of extremely powerful machine with high capacity & faster computing ability
5. Storage capacity
1. Students will develop relevant programming abilities.
2. Students will demonstrate proficiency with statistical analysis of data.
3. Students will develop the ability to build and assess data-based models.
4. Students will execute statistical analyses with professional statistical software.
5. Students will demonstrate skill in data management.
6. Students will apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively
1. Develop relevant programming abilities.
2. Demonstrate proficiency with statistical analysis of data.
3. Develop the ability to build and assess data-based models.
4. Execute statistical analyses with professional statistical software.
5. Demonstrate skill in data management.
· Data Science Introduction
o What is Data Science?
o Where is Data Science Needed?
o How Does a Data Scientist Work?
· What is Data?
o Unstructured and Structured Data
o How to Structure Data
· DataScience & Python
o Python Libraries
· Numpy(Numerical Python)
o What is Numpy
o Why use Numpy
o Installation of Numpy
o Import Numpy
o Creating Arrays
o Array Indexing
o Array Slicing
o Numpy Data Types
o Numpy Array Shape
o Array Reshape
o Array Iterating
o Joining Array
o Numpy Splitting Array
o Array Search
o Array Sort
o Array Filter
o Numpy Random
o Pandas Series
o Pandas DataFrames
o Read csv
o Read json
o Analysing Data
o Cleaning Data
§ Cleaning Empty Cells
§ Cleaning Wrong Format
§ Cleaning Wrong Data
§ Removing Duplicates
o Pandas Correlation
o Dealing With Rows and Columns in CSV File
o TimeSeries Data Analysis
o Grouping Data
o Merging, Joining and Concatenating
o Pie Charts
· Box Plots
· Heat Maps
o Sparse Data
o Spatial Data
o SciPy Statistical Significance tests
· Data Science Functions
· Data Preparation
· Data Science Math
o Linear Functions
o Plotting Functions
o Slope and Intercept
· Data Science Statistics
o Introduction to Statistics
o Stat Percentiles
o Standard Deviation
o Correlation Matrix
· Data Science with SQL
o Getting Started and Selecting & Retrieving Data with SQL
o Filtering, Sorting, and Calculating Data with SQL
o Modifying and Analyzing Data with SQL
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