Data Science with Python

Teacher

admin

(All course fees are in USD)

Course Description

Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. You will also get equipped to master the essential tools of Data Science with Python

Course Delivery

Total online blended learning: 60+ hours

Benefits

Data Science is evolving field & Python has become required skill for significant portion of jobs in Data Science.
5 industry-based projects
Interactive learning with Jupyter notebooks labs
1 year access

Award upon Successful Completion

Data Science with Python Certificate of Achievement

Awarding Organisation

Simplilearn

Learning Outcomes

Explain fundamentals of data science & practical applications.
Explore processes of data preparation, model building, & evaluation.
Apply Python concepts like strings & comprehensively understand Lambda
functions & lists.
Develop solid understanding of fundamentals of NumPy.
Explore array indexing & slicing techniques.
Apply principles of linear algebra in data analysis.
Understand the application of calculus in linear algebra.
Calculate measures of central tendency &d dispersion.
Gain clear understanding of statistical concepts such as skewness, covariance,
& correlation.
Describe null hypothesis & alternative hypothesis.
Examine different hypothesis tests, including Z-test and T-test.
Understand the concept of ANOVA.
Work with pandas’ two primary data structures: Series and DataFrame.
Utilize pandas for tasks such as data loading, indexing, reindexing, and data
merging.
Prepare, format, normalize, & standardize data using data binning
techniques.
Create visualizations with Matplotlib, Seaborn, Plotly, and Bokeh.

Course End Projects

Project 1:Sales Analysis for Business Growth

Analyze the sales data of a retail clothing company and support management in formulating their sales & growth strategy

Project 2: Marketing Campaign Analysis

Perform exploratory data analysis & hypothesis testing to better understand
various factors contributing to customer acquisition.

Project 3: Real Estate Data Visualization

Analyze the housing dataset using various types of plots to gain insights
into the data.

Project 4: Housing Price Analysis

Analyze housing data to uncover insights into house prices, comprehend elements influencing them, & understand impact of various house
features on their price.

Project 5: Customer Behaviour Analysis

Utilize various probability distributions to analyze customer behaviors & store
performance metrics using a custom dataset

Who Should Enrol

Analytics professionals willing to work with Python
Software and IT professionals interested in analytics
Anyone with a genuine interest in data science

Prerequisites

Undergraduate degree or a high school diploma

Course Overview

Lesson 01 – Course Introduction

Lesson 02 – Introduction to Data Science

Lesson 03 – Numpy

Lesson 04 – Working with Pandas

Lesson 05 – Data Visualization

Lesson 06 – Maths and Statistics Fundamentals

Lesson 07 – Probability Distribution

Lesson 08 – Advanced Statistics

Lesson 09 – Data Wrangling

Lesson 10 – Feature Engineering

Accessible Period of Course

1 year from date of enrolment

Customer Reviews

Prachi
Sr Manager – Digitalization & Innovation
The course was well structured. My instructor, Tim, was efficient
and interactive. He ensured that all the queries got addressed
without a miss—overall, it was an excellent learning experience.

Jyothish Chandran
Manager
A very well-experienced trainer, I enjoyed Tim’s sessions. The
way he teaches and progresses in each class is simply superb.
Classes are blended with realistic and easily understandable
examples. Thanks, Tim, for all your efforts to keep us informed
well and for sharing your expertise

Arvind Kumar

Technology Lead

It was a great learning experience. My trainer, Vaishali delivered each session well. All topics were explained with in-depth theory, real-time examples, and execution of the same in Python. Her teaching methodology enhanced the learning process.

Vignesh Manikandan

The online classes were well-paced and helped us learn a ton of stuff within a short amount of time. Vaishali is very knowledgeable and handled all the sessions with outstanding professionalism. Thanks for your expertise

Darshan Gajjar

I learned a lot about Python, Numpy, Pandas, Visualization. The instructor, Swagat was excellent in explaining things clearly. The support team is also accommodative and resolves issues instantly.

Aashish Kumar

….. The faculty, Prashanth Nair, was extremely knowledgeable, and the entire class appreciated his way of teaching…..Support team was very accommodating and quick in providing responses. I was able to grab a 30% hike in my salary after getting certified.

Nikhil Lohakare

The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant

C Muthu Raman

….. a brilliant platform to acquire new & relevant skills at ease. Well laid out course content and expert faculty ensure an excellent learning experience.

Surendaran Baskaran

The instructor is knowledgeable and shares their skills and knowledge. My learning experience has been outstanding with Simplilearn. The practice labs and materials are helpful for better learning. Thank you, Simplilearn. Happy Learning!!

Mukesh Pandey

….. an excellent platform for online learning. Their course curriculum is comprehensive and up to date. We get lifetime access to the recorded sessions in case we need to refresh our understanding. If you are looking to upskill….

Dastagiri Durgam

Incredible mentorship, and amazing and unique lectures. …..provides a great way to learn with self-paced videos and recordings of online sessions. …..

Shiv Sharma

Prashant Nair is an awesome faculty. The way he simplifies, relates and explains topics is outstanding. I would love to enroll for and attend all his classes.

Akash Raj

Technology Engineer

The instructor not only delivers the lecture but also focuses on practical aspects related to the subject. This is something about the course that really impressed me.

Shweta Chauhan

Thanks a lot, Sunny, for the immense support and guidance throughout the project, and for your patience while calmly helping me fix both small and big problems. You have excellent and in-depth knowledge about Python and the alternative options you taught me. I’m delighted to share my opinion about my experience.

Satabdi Adhikary

……courses are affordable and helped me learn something new during the lockdown. Moreover, I also got to add a Well-Known Global Name like Simplilearn to my resume. I could choose the trainer as well as enroll for multiple sessions using the Flexible Pass.

*Note: We reserve the right to revise/change any of the course content &/or instructor at our sole & absolute discretion, without prior notice to learner.

Course Features

Students 1 student
Max Students2000
Duration60 hour
Skill levelall
LanguageEnglish
Re-take course10000

Lesson 01 - Course Introduction

Get started by understanding course components and topics covered. This will help you to be prepared for the upcoming sessions

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Lesson 02 - Introduction to Data Science

Embark on comprehensive journey through data science process, starting with introduction to fundamental concepts. Delve into Python’s role in data science, exploring essential packages & tools used for data manipulation, analysis, and visualization. By understanding the types of plots commonly used in data visualization, along with practical examples, you will acquire skills necessary to effectively analyze and communicate insights from diverse datasets

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Lesson 03 - Numpy

You will comprehensively understand NumPy, fundamental library for numerical computing in Python. Explore array object & attributes, mastering essential array functions, arithmetic operations, & statistical functions for efficient data manipulation & analysis. Additionally, you will delve into advanced topics such as string manipulation, array indexing, and slicing, equipping them with necessary skills to work effectively with NumPy arrays in various data science applications.

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Lesson 04 - Working with Pandas

You will gain a comprehensive understanding of pandas, powerful library for data manipulation & analysis in Python. Explore fundamental data structures such as Series and DataFrame, mastering essential statistical operations and handling techniques for dates, times, categorical data, and text data. Additionally, delve into advanced functionalities, including iteration, sorting, and plotting with Pandas, equipping them with the skills needed to process and analyze diverse datasets efficiently

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Lesson 05 - Data Visualization

Through these topics, you will gain proficiency in data visualization using Matplotlib & Seaborn, two powerful libraries in Python. You will learn to create various types of plots, including line plots, scatter plots, bar charts, box plots, radar charts, area plots, polar plots, tree maps, and pie charts using Matplotlib. Additionally, using Seaborn, you will explore advanced visualization techniques such as 3D visualization, violin plots, pair plots, heatmaps, joint plots, swarm plots, and 3D graphs with multiple columns.

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Lesson 06 - Maths and Statistics Fundamentals

This comprehensively explores linear algebra, calculus, & statistics — foundational pillars of data science. Grasp essential concepts such as scalars, vectors, matrices, & operations, along with understanding norms, ranks, determinants, inverses, eigenvalues, & eigenvectors. Furthermore, delve into application of calculus within linear algebra, establishing a solid mathematical framework for data analysis. Additionally, uncover importance of statistics in data science, mastering various types of data & crucial statistical measures, including central tendency, dispersion, shape, covariance, and correlation. By mastering these concepts, you will be able to manipulate and analyze complex datasets, extract meaningful insights, and make informed decisions in data-driven environments

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Lesson 07 - Probability Distribution

You will explore core principles of probability theory essential for data science. Understand random variables, probability distributions (both discrete & continuous), & key concepts like probability density functions & cumulative distribution functions. Additionally, delve into crucial theorems like the Central Limit Theorem and Bayes’ Theorem, along with estimation theory, equipping them to make informed statistical inferences and extract valuable insights from data.

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Lesson 08 - Advanced Statistics

You will master hypothesis testing methods essential for data analysis. You will understand concepts like null & alternative hypotheses, confidence intervals, margin of error, & confidence levels. Additionally, you will explore distributions, including the standard normal distribution (Z-distribution), t-distribution, & chi-square distribution, along with associated tests like t-test, z-test, and f-test. By understanding these techniques, you can make statistically sound decisions, analyze variance, & draw reliable conclusions from data

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Lesson 09 - Data Wrangling

Through these topics, you will acquire essential data preparation and manipulation skills, crucial steps in the data analysis pipeline. Learn the importance of thorough data collection and inspection, techniques to handle duplicates, and strategies for cleaning messy datasets. Additionally, delve into data transformation, binning, and outlier detection methods to ensure data quality and reliability

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Lesson 10 - Feature Engineering

Learners will explore the fundamentals of feature engineering, a critical aspect of data preprocessing in machine learning. They will learn various methods for transforming variables, including feature scaling, label encoding, one-hot encoding, and hashing, essential for preparing categorical and numerical data for model training. Additionally, learners will delve into grouping operations, enabling them to aggregate and summarize data efficiently. By mastering these techniques, learners will be equipped to engineer informative features from raw data, enhancing machine learning models’ predictive power and performance.

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