Data Science
Data has emerged as the ‘new oil’ and ‘new soil’ of the digital economy. Enterprise success now hinges on the ability to extract business insights from the unprecedented flow of data. At one end, data science applications help enterprises see meaning out of information and take strategic decisions. On the other end, innovative data science applications are improving efficiencies, precision & accuracies dramatically, by automating repetitive tasks.
Enterprises are increasingly adopting Data Science technologies in the disciplines of artificial intelligence (AI), machine learning (ML), and deep learning, to name a few. There is an exponentially growing demand in the market for data analysts & data scientists.
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Python
Python is a popular programming language used in data science applications. It is a general purpose open-source programming language with a vast repository of data science & analytics packages to choose from. Python has very good data visualisation options and has emerged as the most preferred programming language for machine learning & deep learning.
Pandas
Pandas is a fast, powerful, flexible, and easy to use open-source data analysis and manipulation tool, built on the Python programming language. Since its emergence in 2010, it has enabled Python to be a powerful and productive data analysis environment.
Pandas provide tools for reading and writing data into data structures and files. It also provides powerful aggregation functions to manipulate data. Pandas provides functions for performing operations like merging, reshaping, joining, and concatenating data. Pandas also provides visualizations options based on Matplotlib.
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Exploratory Data Analysis (using Pandas, Python)
In this course, we will learn to apply exploratory data analysis (EDA), to extract descriptive statistical information from the data and further use graphical tools to conduct diagnostic studies, which helps the managements to make informed business decisions. Business Analysts are expected to perform these activities routinely & proficiently.
This course is a continuation from our course on basic Python and it covers many important concepts in data processing to prepare you as a business analyst.
We will also solve many live exercises during the workshop to ensure learning is immediately applied & internalised.
By the end of this course, you will develop many skills necessary to perform the demanding task of a data analyst. It will also provide you with a strong foundation for the more advanced learnings, should you choose a career of a data scientist.
Course Contents:
- Overview of Analytics
- Introduction to Exploratory Data Analysis
- Basic Statistics
- Introduction to Pandas
- Pandas Data Structures – Series, DataFrame
- Data Preparation
- Data Import
- Meta data inspection, Data Type conversion
- Data Understanding (Statistical summary, Identification of Missing Values / Outliers / Duplicates)
- Subsetting (Columns) and Indexing / Filtering (Rows)
- Handling of Missing Values / Outliers / Duplicates
- Reordering, Calculated Columns, Renaming, Deleting, Sorting
- User Defined Functions: Apply & Applymap
- Date & Time handling
- Combining the datasets: Join/merge, Append, Concatenate
- Grouping / Binning
- Summaries / Pivot Tables
- Graphical Analysis
- Pandas / Seaborn / Matplotlib
- Selection of an appropriate chart
- Proportion charts (Pie, Doughnuts charts, 100% stacked bar and column chart)
- line charts, Box Plot, Histogram, Frequency Plot, Frequency Polygon chart, Area Chart
- Clustered and Stacked charts (Bar and Column Charts)
- Scatter plot, Heatmap
Who should attend:
- Professionals from the enterprises who do not want to be left out of ‘Big Data’ revolution.
- Professionals who have newly joined enterprises already applying ‘Big Data’ tools.
- Individual professionals who want to make career in data analysis, or data science disciplines.
- Anyone who wants to learn data analysis with Python language.
- Minitab or Excel users who wants to enhance data analysis skills.
