about
Data Analysis & Visualization with Python
The primary aim of this guide is to demystify the concept of data science and teach you how to get started on becoming a successful data scientist (or, at least, a solid candidate). The world of data science is vast and complex. It’s a field that requires many skill sets, from programming to statistics and machine learning. It is also an ever-evolving discipline,
with new methodologies emerging every year and technologies constantly being adopted by businesses in their quest for growth. In today’s world, data science is the new trend that is sweeping the globe.
<strong><span style=”color: #ff0000;”>about </span></strong>
<h2><strong>Data Analysis & Visualization with Python</strong></h2>
The primary aim of this guide is to demystify the concept of data science and teach you how to get started on becoming a successful data scientist (or, at least, a solid candidate). The world of data science is vast and complex. It’s a field that requires many skill sets, from programming to statistics and machine learning. It is also an ever-evolving discipline,
with new methodologies emerging every year and technologies constantly being adopted by businesses in their quest for growth. In today’s world, data science is the new trend that is sweeping the globe.
Data scientists are the professionals who are responsible for understanding and analyzing data sets to find insights. They use statistics, machine learning and other tools to extract knowledge from data. The data scientist is a relatively new profession in the world of business, one that has only been around since the late 1990s. The field is still evolving, with new technologies paving the way for more advanced methods of analysis. A career in data science is an exciting opportunity for those who have an analytical mind and enjoy working with numbers.
Data scientists work in a number of industries, including finance, healthcare, retail, tech and more.Data science is a relatively new field, with its first graduates in 2002. Yet it has already been predicted that the job market for data scientists will grow by 45% between 2016 and 2024. Large organizations such as IBM and Microsoft have committed to hiring over 10,000 data scientists worldwide over the next 5 years. This is a great opportunity for recent graduates without an advanced degree in a related field to trade into these positions.
<strong>FUTURE IN DATA SCIENCE</strong>
Data scientists are the professionals who are responsible for understanding and analyzing data sets to find insights. They use statistics, machine learning and other tools to extract knowledge from data. The data scientist is a relatively new profession in the world of business, one that has only been around since the late 1990s. The field is still evolving, with new technologies paving the way for more advanced methods of analysis. A career in data science is an exciting opportunity for those who have an analytical mind and enjoy working with numbers.
Data scientists work in a number of industries, including finance, healthcare, retail, tech and more. Data science is a relatively new field, with its first graduates in 2002. Yet it has already been predicted that the job market for data scientists will grow by 45% between 2016 and 2024. Large organizations such as IBM and Microsoft have committed to hiring over 10,000 data scientists worldwide over the next 5 years. This is a great opportunity for recent graduates without an advanced degree in a related field to trade into these positions.
Course Content
Introduction to Python
Control Structures
Functions
Data Structures
File Handling
Object-Oriented Programming
Exception Handling
Modules and Packages
Basic Data Analysis with Python
Web Scraping with Python
Pandas
NumPy
Matplotlib
Seaborn
SciPy
Scikit-Learn
Introduction to SQL
Data Manipulation with SQL
Data Aggregation with SQL
Data Filtering with SQL
Data Modification with SQL
SQL Functions
Advanced SQL Queries
Working with Views and Stored Procedures
SQL for Data Science
Introduction to Machine Learning
Data Preprocessing
Regression
Classification
Clustering
Dimensionality Reduction
Model Evaluation and Selection
Deep Learning
Natural Language Processing
Recommendation Systems
Introduction to Deep Learning
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Deep Learning for Computer Vision
Deep Learning for Natural Language Processing
Generative Models
Reinforcement Learning
Model Evaluation and Selection
Applications of Deep Learning
Introduction to Tableau
Connecting to Data Sources
Building Visualizations
Creating Dashboards
Advanced Visualization Techniques
Data Analysis and Exploration
Sharing and Collaborating
Tableau for Data Science
Course Content
Introduction to Python
Control Structures
Functions
Data Structures
File Handling
Object-Oriented Programming
Exception Handling
Modules and Packages
Basic Data Analysis with Python
Web Scraping with Python
Pandas
NumPy
Matplotlib
Seaborn
SciPy
Scikit-Learn
Introduction to SQL
Data Manipulation with SQL
Data Aggregation with SQL
Data Filtering with SQL
Data Modification with SQL
SQL Functions
Advanced SQL Queries
Working with Views and Stored Procedures
SQL for Data Science
Introduction to Machine Learning
Data Preprocessing
Regression
Classification
Clustering
Dimensionality Reduction
Model Evaluation and Selection
Deep Learning
Natural Language Processing
Recommendation Systems
Introduction to Deep Learning
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Deep Learning for Computer Vision
Deep Learning for Natural Language Processing
Generative Models
Reinforcement Learning
Model Evaluation and Selection
Applications of Deep Learning
Introduction to Tableau
Connecting to Data Sources
Building Visualizations
Creating Dashboards
Advanced Visualization Techniques
Data Analysis and Exploration
Sharing and Collaborating
Tableau for Data Science
