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Description

Welcome to Data Analysis Analytics Bootcamp content powered by TakenMind.
Are you interested to learn how zetabytes of data are processed by top tech companies to analyse data inorder to boost their business growth? Well, for a beginner you are at the right place and this is the most probably the right time for you to learn this. 
The average data scientist today earns $123,000 a year, according to Indeed research. But the operating term here is “today,” since data science has paid increasing dividends since it really burst into business consciousness in recent years.
This course has its base on financial Analysis and the following concepts are covered:
Python Fundamentals
Pandas for Efficient Data Analysis
NumPy for High Speed Numerical Processing
Matplotlib for Data Visualization
Pandas for Data Manipulation and Analysis
Seaborn Data Visualization
Worked-up examples.
Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
You will learn how to:
Import data sets
Clean and prepare data for analysis
Manipulate pandas DataFrame
Summarize data
Build machine learning models using scikit-learn
Build data pipelines
Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:
Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
Who this course is for:
Beginner Python Data Analyst should take up this course.
Intermediate Python Data Analyst should take up this course.

What you'll learn

Perform Data Analytics seamlessly and smartly

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Setup and Jupyter Environment (Python 3)
5
1.1-Introduction to the Study Kit
1.2-#1 Downloading Setup and Installation
1.3-#2 Installing Work Environment - Jupyter Notebook
1.4-#3 Exploring Jupyter Notebook functionalities
1.5-#4 Python Package Index - Using Command line interface and Jupyter Notebook
2-Data Manipulation with Numpy (Python 3)
8
2.1-#1 Getting Started - Numpy Arrays (Numerical Python)
2.2-#2 Scalar Operations on Numpy Arrays
2.3-#3 Array Indexes - Part 1
2.4-#4 Array Indexes in Multi-Dimensional Numpy Arrays
2.5-#5 - Premium Array Operations
2.6-#6 Saving And Loading Arrays To External Memory
2.7-#7 Statistical Processing And Sketching Graphs
2.8-#8 Conditional Clauses And Boolean Operation
3-Data Manipulation with Pandas (Python 3)
10
3.1-#1 Getting Started with Series
3.2-#2 Introduction to DataFrames in Pandas
3.3-#3 Learning to access elements with indexes
3.4-#4 - Re-indexing in pandas Series and Dataframes
3.5-#5 - Dropping values from Series and DataFrames
3.6-#6 - Handling Null or NAN values in pandas
3.7-#7 Selecting and Modifying entries in Pandas
3.8-#8 Coordinate and Regulate data in Series and Dataframes
3.9-#9 - Ranking and Sorting in Series
3.10-#10 Statistical Data Analysis and Graphs in Pandas
4-Starting with File Operations (Python 3)
2
4.1-#1 File Operations - Dataframes And Csv
4.2-#2 Import Data From Excel File
5-Data Analysis and Methodologies - Learn to perform Operations on datasets (Py 3)
10
5.1-#1 Pandas - Merging along columns in DataFrames
5.2-#2 Concatenation of Arrays, Series and Dataframes
5.3-#3 Combining values of a DataFrame or Series
5.4-#4 Reshaping Datasets - Series and Dataframe
5.5-#5 Pivot Tables
5.6-#6 Duplicates Analysis in dataset
5.7-#7 Mapping in DataFrame
5.8-#8 Replace values in Series
5.9-#9 Renaming Indexes in DataFrame
5.10-#10 Observation, Filtering and Basic Analysis
6-Data Visualization
10
6.1-Data Visualization and Introduction to Seaborn Visualization Library
6.2-Histogram Visualization in seaborn
6.3-Seaborn Kernel Density Estimation (KDE) Plot on Univariates
6.4-Seaborn KDE Plot for multivariates
6.5-Plotting multiple charts with seaborn
6.6-Box Plot Visualization
6.7-Regression Plots with seaborn
6.8-Violin plot Visualization
6.9-Heat Maps Visualization
6.10-Cluster Map Visualization