Data Analytics training in Ahmedabad

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 analysis, create meaningful data visualizations, predict future trends from data, and more!

Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. 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.

What is Data Analytics.

As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.

The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.

The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics.

Why you need to learn Data Analytics?

The economy is starting to shift due to data. Look at Amazon’s rise to the top, and the effect the company has had on retail. It’s an impact that other fields, such as the civic sector, are now trying to replicate.

Why you should study data analysis is simple: Data analysis is the future, and the future will demand skills for jobs as functional analysts, data engineers, data scientists, and advanced analysts. According to O*NET, the projected growth for data analysts is 8% between 2019-2029. On average, data analysts earned $94,280 in 2019. However, salary compensation for data analysts varies depending on where they work and what industry they work in.

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WHY YOU SHOULD REGISTER AT LOGICRAYS FOR MACHINE LEARNING IN AHMEDABAD?

Help to get knowledge of core Data Analysis concepts
Get the core knowledge of Data Interpretation
Learn to get useful insight from data and applying predictive algorithm on it.
Proven and well tested teaching methods for beginner
Get mentored by industry best Data Analytics expert
Learn to develop industry standard Data Analytics projects

If you are planning to learn Python from the professionals, get in touch with us now!

For any questions, you can contact us or visit LogicRays Academy center.

Data Analytics Course Details

Introduction to Python

First steps with python
Python variables and data-types

Moving Forward with Python

Branching and loops
Function and scope

Python OS and Filesystem

Numerical Computing with Numpy

Data Analysis using Pandas

Data Visualization with Matplotlib and Seaborn

Data visualization guide
Data visualization using matplotlib

Understanding about Data with Python

Introduction to Data
Univariate Data
Multivariate Data
Populations and Samples

Inferential Statistical Analysis with Python

Confidence Interval
Hypothesis Testing
Case studies & applications of the inferential procedures

Exploratory Data Analysis

Exploring stackoverflow survey data

Applying Statistical Model using Python

Introduction of Statistical Model
Fitting Models to Independent Data
Fitting Models to Dependent Data

Machine Learning for Data Analytics

Machine Learning Model Evaluation

Model Evaluation & Selection
Confusion Matrics
Classifier Decision Function
Precision-recall and ROC curves
Multi-Class Evaluation
Regression Evaluation
Model Selection: Optimizing Classifiers for Different Evaluation Metrics

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