JumpStart To Python For Data Science Training

JumpStart to Python for Data Science is a training program that is growing a new knowledge domain very vastly. In this course, professionals will learn to solve core data science problems using Python.

📘 Python 👥 1543 Enrolled ⏱️ 3 Days 💼 Intermediate Level ⭐ 4.5 | 113 Reviews

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Course Overview

Python for data sciences offers professionals a cross-platform, fast, mature, and reliable environment for data analysis, algorithmic problem-solving, and machine learning.

Professionals will choose their careers as data scientists to work with data and derive value from the data.

For the data science practitioner, the Python programming language is an indispensable tool.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

What you will learn

Manage data and perform linear algebra in Python
Derive inferences from the analysis by performing inferential statistics
Solve data science problems in Python
Create high-end visualizations using Python
Evaluate and apply the linear regression technique to estimate the relationships among variables
Build recommendation engines with the various collaborative filtering algorithms
Apply the ensemble methods to improve your predictions
Work with big data technologies to handle data at scale

Who Should Attend This Course?

Data Scientists, Data Analysts, Software Engineers, Data Engineers, and Developers.

Prerequisites

Written Python scripts
Be comfortable working with files, folders, and the command line

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📘 JumpStart To Python For Data Science Outline

The world of arrays with NumPy
Empowering data analysis with pandas
Data cleansing
Data operations

Various forms of distribution
A z-score
A p-value
One-tailed and two-tailed tests
Type 1 and Type 2 errors
A confidence interval
Correlation
Z-test vs T-test
The F distribution
The chi-square distribution
The chi-square test of independence
ANOVA

What is data mining?
Presenting an analysis

Controlling the line properties of a chart
Creating multiple plots
Playing with text
Styling your plots
Box plots
Heatmaps
Scatter plots with histograms
A scatter plot matrix
Area plots
Bubble charts
Hexagon bin plots
Trellis plots
A 3D plot of a surface

Different types of machine learning
Decision trees
Linear regression
Logistic regression
The naive Bayes classifier
The k-means clustering
Hierarchical clustering

Simple linear regression
Multiple regression
Training and testing a model

Logistic regression

Recommendation data
User-based collaborative filtering
Item-based collaborative filtering

The census income dataset
Decision trees
Random forests
Applying Segmentation with k-means Clustering
The k-means algorithm and its working
The k-means clustering with countries
Clustering the countries

Preprocessing data
Creating a wordcloud
Word and sentence tokenization
Parts of speech tagging
Stemming and lemmatization
The Stanford Named Entity Recognizer
Performing sentiment analysis on world leaders using Twitter

What is Hadoop?
Python MapReduce
File handling with Hadoopy
Pig
Python with Apache Spark

Still have questions?

Reach out to our learning advisors for personalized guidance on choosing the right course, group training, or enterprise packages.

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What You Get with Microtek Learning

Instructor-Led Excellence

✓ Certified Instructor-led Training
✓ Top Industry Trainers
✓ Official Student Handbooks

Measurable Learning Outcomes

✓ Pre- & Post-Training Assessments
✓ Practice Tests
✓ Exam-Oriented Curriculum

Real-World Skill Building

✓ Hands-on Activities & Scenarios
✓ Interactive Online Courses
✓ Peer Collaboration (Not in self-paced)

Full Support & Perks

✓ Exam Scheduling Support ^*
✓ Learn & Earn Program ^*
✓ Support from Certified Experts
✓ Gov. & Private Pricing ^*

Our Clients

For over 10 years, Microtek Learning has helped organizations, leaders, students and professionals to reach their maximum potential. We have led the path by addressing their challenges and advancing their performances.