Data Science with Python Training in Saint Paul

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Data Science with Python Course Overview

The Data Science with Python course empowers you to excel in Python programming. In this course, you'll delve into data science, data analysis, data visualization, data wrangling, feature engineering, and statistics. Upon finishing the course, you'll excel in using essential data science tools with Python.

Data Science with Python Training Key Features

100% Money Back Guarantee

No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Data Science with Python course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!

60+ hours of blended learning

40+ Assisted practices and lesson-wise knowledge checks

Lifetime access to self-paced learning content

Industry-based projects for experiential learning

Interactive learning with Jupyter notebooks labs

Dedicated live sessions by faculty of industry experts

60+ hours of blended learning

Industry-based projects for experiential learning

40+ Assisted practices and lesson-wise knowledge checks

Interactive learning with Jupyter notebooks labs

Lifetime access to self-paced learning content

Dedicated live sessions by faculty of industry experts

60+ hours of blended learning

Industry-based projects for experiential learning

40+ Assisted practices and lesson-wise knowledge checks

Interactive learning with Jupyter notebooks labs

Lifetime access to self-paced learning content

Dedicated live sessions by faculty of industry experts

Skills Covered

Data wrangling

Data visualization

Web scraping

Python programming concepts

ScikitLearn package for Natural Language Processing

Data exploration

Mathematical computing

Hypothesis building

NumPy and SciPy package

Data wrangling

Data exploration

Data visualization

Mathematical computing

Web scraping

Hypothesis building

Python programming concepts

NumPy and SciPy package

ScikitLearn package for Natural Language Processing

Data wrangling

Data exploration

Data visualization

Mathematical computing

Web scraping

Hypothesis building

Python programming concepts

NumPy and SciPy package

ScikitLearn package for Natural Language Processing

Begin your journey to success

Get lifetime access to self-paced e-learning content

View other cohorts

Benefits

The Python for Data Science Training in Saint Paul course will be your guide to learning how to use Python. The recent US Bureau of Labor Statistics report suggests that towards the end of 2026, 11.6 professional data science jobs will be available and individuals who have the requisite skills and knowledge, particularly in Python will be able to grab the right roles and rise up faster.

Designation
Annual Salary
Hiring Companies

Data Analyst
Annual Salary
$43KMin
$62KAverage
$95KMax
Source: Glassdoor
Hiring Companies
Source: Indeed
Data Scientist
Annual Salary
$83KMin
$113KAverage
$154KMax
Source: Glassdoor
Hiring Companies
Source: Indeed

Training Options

Self Paced Learning

Lifetime access to high-quality self-paced eLearning content curated by industry experts
2 hands-on projects and 8+ demo projects to perfect the skills learned
3 simulation test papers for self-assessment
Lab access to practice live during sessions
24x7 learner assistance and support

23% Off$650$845

online Bootcamp

Everything in Self-Paced Learning, plus
Flexi Pass Enabled: Flexibility to reschedule your cohort within first 90 days of access.

Live, online classroom training by top instructors and practitioners

Batch starting in Saint Paul from:

12^th May, Weekday Class

24^th May, Weekend Class

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33% Off$750$1,125

Corporate Training

Upskill or reskill your teams

Flexible pricing & billing options
Private cohorts available
Training progress dashboards
Skills assessment & benchmarking
Platform integration capabilities
Dedicated customer success manager

Data Science with Python Course Curriculum

Eligibility

The Python for Data Science training in Saint Paul is the best course for those who want to jump into the field of Data Science. This course is also valuable for IT professionals and experienced software developers, in addition to anyone who's interested in Data Science.

Pre-requisites

The Python for Data Science course in Saint Paul is a resource that should be explored by professionals who have previous programming experience, as well as knowledge of relevant topics like Math Refresher, Data Science in Python, Statistics Essentials for Data Science, and Data Science in Real Life. These concepts will also be covered in the course.

Course Content

Data Science with Python
Preview
- Lesson 01 - Course Introduction
  Preview
  - 1.01 Course Introduction
    03:06
  - 1.02 What you will Learn
    01:53
- Lesson 02 - Introduction to Data Science
  Preview
  - 2.01 Introduction
    00:44
  - 2.02 Data Science and its Applications
    02:41
  - 2.03 The Data Science Process: Part 1
    02:15
  - 2.04 The Data Science Process: Part 2
    02:02
  - 2.05 Recap
    00:34
- Lesson 03 - Essentials of Python Programming
  Preview
  - 3.01 Introduction
    00:58
  - 3.02 Setting Up Jupyter Notebook: Part 1
    02:02
  - 3.03 Setting Up Jupyter Notebook: Part 2
    04:14
  - 3.04 Python Functions
    03:57
  - 3.05 Python Types and Sequences
    04:50
  - 3.06 Python Strings Deep Dive
    07:16
  - 3.07 Python Demo: Reading and Writing csv files
    06:25
  - 3.08 Date and Time in Python
    02:34
  - 3.09 Objects in Python Map
    07:42
  - 3.10 Lambda and List Comprehension
    03:53
  - 3.11 Why Python for Data Analysis?
    02:09
  - 3.12 Python Packages for Data Science
    02:44
  - 3.13 StatsModels Package: Part 1
    02:38
  - 3.14 StatsModels Package: Part 2
    03:29
  - 3.15 Scipy Package
    02:47
  - 3.16 Recap
    00:51
  - 3.17 Spotlight
    01:49
- Lesson 04 - NumPy
  Preview
  - 4.01 Introduction
    00:51
  - 4.02 Fundamentals of NumPy
    02:49
  - 4.03 Array shapes and axes in NumPy: Part A
    03:27
  - 4.04 NumPy Array Shapes and Axes: Part B
    03:28
  - 4.05 Arithmetic Operations
    02:35
  - 4.06 Conditional Logic
    02:48
  - 4.07 Common Mathematical and Statistical Functions in Numpy
    04:29
  - 4.08 Indexing And Slicing: Part 1
    02:27
  - 4.09 Indexing and Slicing: Part 2
    02:28
  - 4.10 File Handling
    02:24
  - 4.11 Recap
    00:33
- Lesson 05 - Linear Algebra
  Preview
  - 5.01 Introduction
    00:51
  - 5.02 Introduction to Linear Algebra
    02:46
  - 5.03 Scalars and Vectors
    01:50
  - 5.04 Dot Product of Two Vectors
    02:37
  - 5.05 Linear independence of Vectors
    01:05
  - 5.06 Norm of a Vector
    01:30
  - 5.07 Matrix
    03:28
  - 5.08 Matrix Operations
    03:14
  - 5.09 Transpose of a Matrix
    00:59
  - 5.10 Rank of a Matrix
    02:11
  - 5.11 Determinant of a matrix and Identity matrix or operator
    02:51
  - 5.12 Inverse of a matrix and Eigenvalues and Eigenvectors
    02:45
  - 5.13 Calculus in Linear Algebra
    01:34
  - 5.14 Recap
    00:48
- Lesson 06 - Statistics Fundamentals
  Preview
  - 6.01 Introduction
    01:00
  - 6.02 Importance of Statistics with Respect to Data Science
    02:34
  - 6.03 Common Statistical Terms
    01:46
  - 6.04 Types of Statistics
    02:50
  - 6.05 Data Categorization and Types
    03:20
  - 6.06 Levels of Measurement
    02:37
  - 6.07 Measures of Central Tendency
    01:51
  - 6.08 Measures of Central Tendency
    01:48
  - 6.09 Measures of Central Tendency
    01:02
  - 6.10 Measures of Dispersion
    02:19
  - 6.11 Random Variables
    02:17
  - 6.12 Sets
    02:40
  - 6.13 Measures of Shape (Skewness)
    02:16
  - 6.14 Measures of Shape (Kurtosis)
    01:52
  - 6.15 Covariance and Correlation
    02:44
  - 6.16 Recap
    00:54
- Lesson 07 - Probability Distribution
  Preview
  - 7.01 Introduction
    01:02
  - 7.02 Probability,its Importance, and Probability Distribution
    03:36
  - 7.03 Probability Distribution : Binomial Distribution
    02:53
  - 7.04 Probability Distribution: Poisson Distribution
    02:29
  - 7.05 Probability Distribution: Normal Distribution
    04:19
  - 7.06 Probability Distribution: Uniform Distribution
    01:30
  - 7.07 Probability Distribution: Bernoulli Distribution
    03:05
  - 7.08 Probability Density Function and Mass Function
    02:33
  - 7.09 Cumulative Distribution Function
    02:26
  - 7.10 Central Limit Theorem
    02:57
  - 7.11 Estimation Theory
    02:49
  - 7.12 Recap
    00:39
- Lesson 08 - Advanced Statistics
  Preview
  - 8.01 Introduction
    01:07
  - 8.02 Distribution
    01:45
  - 8.03 Kurtosis Skewness and Student's T-distribution
    02:32
  - 8.04 Hypothesis Testing and Mechanism
    02:25
  - 8.05 Hypothesis Testing Outcomes: Type I and II Errors
    01:54
  - 8.06 Null Hypothesis and Alternate Hypothesis
    01:47
  - 8.07 Confidence Intervals
    02:01
  - 8.08 Margins of error
    01:49
  - 8.09 Confidence Level
    01:31
  - 8.10 T - Test and P - values (Lab)
    04:50
  - 8.11 Z - Test and P - values
    05:33
  - 8.12 Comparing and Contrasting T test and Z test
    03:45
  - 8.13 Bayes Theorem
    02:24
  - 8.14 Chi Sqare Distribution
    03:16
  - 8.15 Chi Square Distribution : Demo
    03:25
  - 8.16 Chi Square Test and Goodness of Fit
    02:46
  - 8.17 Analysis of Variance or ANOVA
    02:41
  - 8.18 ANOVA Termonologies
    02:08
  - 8.19 Assumptions and Types of ANOVA
    02:53
  - 8.20 Partition of Variance using Python
    03:06
  - 8.21 F - Distribution
    02:41
  - 8.22 F - Distribution using Python
    03:59
  - 8.23 F - Test
    03:09
  - 8.24 Recap
    01:19
  - 8.25 Spotlight
    02:35
- Lesson 09 - Pandas
  Preview
  - 9.01 Introduction
    00:52
  - 9.02 Introduction to Pandas
    02:15
  - 9.03 Pandas Series
    03:37
  - 9.04 Querying a Series
    04:01
  - 9.05 Pandas Dataframes
    03:05
  - 9.06 Pandas Panel
    01:46
  - 9.07 Common Functions In Pandas
    02:56
  - 9.08 Pandas Functions Data Statistical Function, Windows Function
    02:18
  - 9.09 Pandas Function Data and Timedelta
    02:57
  - 9.10 IO Tools Explain all the read function
    03:15
  - 9.11 Categorical Data
    02:52
  - 9.12 Working with Text Data
    03:15
  - 9.13 Iteration
    02:37
  - 9.14 Sorting
    01:19
  - 9.15 Plotting with Pandas
    03:23
  - 9.16 Recap
    00:45
- Lesson 10 - Data Analysis
  Preview
  - 10.01 Introduction
    00:46
  - 10.02 Understanding Data
    02:31
  - 10.03 Types of Data Structured Unstructured Messy etc
    02:35
  - 10.04 Working with Data Choosing appropriate tools, Data collection, Data wrangling
    02:53
  - 10.05 Importing and Exporting Data in Python
    02:42
  - 10.06 Regular Expressions in Python
    08:24
  - 10.07 Manipulating text with Regular Expressions
    06:04
  - 10.08 Accessing databases in Python
    03:32
  - 10.09 Recap
    00:50
  - 10.10 Spotlight
    02:08
- Lesson 11 - Data Wrangling
  Preview
  - 11.01 Introduction
    00:58
  - 11.02 Pandorable or Idiomatic Pandas Code
    06:21
  - 11.03 Loading Indexing and Reindexing
    02:45
  - 11.04 Merging
    05:48
  - 11.05 Memory Optimization in Python
    03:01
  - 11.06 Data Pre Processing: Data Loading and Dropping Null Values
    02:34
  - 11.07 Data Pre-processing Filling Null Values
    02:32
  - 11.08 Data Binning Formatting and Normalization
    04:46
  - 11.09 Data Binning Standardization
    02:19
  - 11.10 Describing Data
    02:17
  - 11.11 Recap
    01:03
- Lesson 12 - Data Visualization
  Preview
  - 12.01 Introduction
    00:58
  - 12.02 Principles of information visualization
    02:27
  - 12.03 Visualizing Data using Pivot Tables
    02:04
  - 12.04 Data Visualization Libraries in Python Matplotlib
    01:56
  - 12.05 Graph Types
    01:36
  - 12.06 Data Visualization Libraries in Python Seaborn
    01:15
  - 12.07 Data Visualization Libraries in Python Seaborn
    02:34
  - 12.08 Data Visualization Libraries in Python Plotly
    01:07
  - 12.09 Data Visualization Libraries in Python Plotly
    02:51
  - 12.10 Data Visualization Libraries in Python Bokeh
    02:16
  - 12.11 Data Visualization Libraries in Python Bokeh
    01:59
  - 12.12 Using Matplotlib to Plot Graphs
    03:32
  - 12.13 Plotting 3D Graphs for Multiple Columns using Matplotlib
    02:14
  - 12.14 Using Matplotlib with other python packages
    03:30
  - 12.15 Using Seaborn to Plot Graphs
    02:18
  - 12.16 Using Seaborn to Plot Graphs
    01:15
  - 12.17 Plotting 3D Graphs for Multiple Columns Using Seaborn
    03:16
  - 12.18 Introduction to Plotly
    03:29
  - 12.19 Introduction to Bokeh
    01:32
  - 12.20 Recap
    00:46
- Lesson 13 - End to End Statistics Application with Python
  Preview
  - 13.01 Introduction
    01:05
  - 13.02 Basic Statistics with Python Problem Statement
    01:06
  - 13.03 Basic Statistics with Python Solution
    11:16
  - 13.04 Scipy for Statistics Problem Statement
    01:11
  - 13.05 Scipy For Statistics Solution
    06:10
  - 13.06 Advanced Statistics Python
    01:10
  - 13.07 Advanced Statistics with Python Solution
    10:56
  - 13.08 Recap
    00:29
  - 13.09 Spotlight
    02:11

Free Course
Statistics Essential for Data Science
Preview
- Lesson 01: Course Introduction
  07:05Preview
  - 1.01 Course Introduction
    05:19
  - 1.02 What Will You Learn
    01:46
- Lesson 02: Introduction to Statistics
  25:49Preview
  - 2.01 Learning Objectives
    01:16
  - 2.02 What Is Statistics
    01:50
  - 2.03 Why Statistics
    02:06
  - 2.04 Difference between Population and Sample
    01:20
  - 2.05 Different Types of Statistics
    02:42
  - 2.06 Importance of Statistical Concepts in Data Science
    03:20
  - 2.07 Application of Statistical Concepts in Business
    02:11
  - 2.08 Case Studies of Statistics Usage in Business
    03:09
  - 2.09 Applications of Statistics in Business: Time Series Forecasting
    03:50
  - 2.10 Applications of Statistics in Business Sales Forecasting
    03:19
  - 2.11 Recap
    00:46
- Lesson 03: Understanding the Data
  17:29Preview
  - 3.01 Learning Objectives
    01:12
  - 3.02 Types of Data in Business Contexts
    02:11
  - 3.03 Data Categorization and Types of Data
    03:13
  - 3.03 Types of Data Collection
    02:14
  - 3.04 Types of Data
    02:01
  - 3.05 Structured vs. Unstructured Data
    01:46
  - 3.06 Sources of Data
    02:17
  - 3.07 Data Quality Issues
    01:38
  - 3.08 Recap
    00:57
- Lesson 04: Descriptive Statistics
  34:51Preview
  - 4.01 Learning Objectives
    01:26
  - 4.02 Descriptive Statistics
    02:03
  - 4.03 Mathematical and Positional Averages
    03:15
  - 4.04 Measures of Central Tendancy: Part A
    02:17
  - 4.05 Measures of Central Tendancy: Part B
    02:41
  - 4.06 Measures of Dispersion
    01:15
  - 4.07 Range Outliers Quartiles Deviation
    02:30
  - 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance
    03:37
  - 4.09 Z Score and Empirical Rule
    02:14
  - 4.10 Coefficient of Variation and Its Application
    02:06
  - 4.11 Measures of Shape
    02:39
  - 4.12 Summarizing Data
    02:03
  - 4.13 Recap
    00:54
  - 4.14 Case Study One: Descriptive Statistics
    05:51
- Lesson 05: Data Visualization
  23:36Preview
  - 5.01 Learning Objectives
    00:57
  - 5.02 Data Visualization
    02:15
  - 5.03 Basic Charts
    01:52
  - 5.04 Advanced Charts
    02:19
  - 5.05 Interpretation of the Charts
    02:57
  - 5.06 Selecting the Appropriate Chart
    02:25
  - 5.07 Charts Do's and Dont's
    02:47
  - 5.08 Story Telling With Charts
    01:29
  - 5.09 Data Visualization: Example
    02:41
  - 5.10 Recap
    00:50
  - 5.11 Case Study Two: Data Visualization
    03:04
- Lesson 06: Probability
  21:51Preview
  - 6.01 Learning Objectives
    00:55
  - 6.02 Introduction to Probability
    03:10
  - 6.03 Probability Example
    02:02
  - 6.04 Key Terms in Probability
    02:25
  - 6.05 Conditional Probability
    02:11
  - 6.06 Types of Events: Independent and Dependent
    02:59
  - 6.07 Addition Theorem of Probability
    01:58
  - 6.08 Multiplication Theorem of Probability
    02:08
  - 6.09 Bayes Theorem
    03:10
  - 6.10 Recap
    00:53
- Lesson 07: Probability Distributions
  24:45Preview
  - 7.01 Learning Objectives
    00:52
  - 7.02 Probability Distribution
    01:25
  - 7.03 Random Variable
    02:21
  - 7.04 Probability Distributions Discrete vs.Continuous: Part A
    01:44
  - 7.05 Probability Distributions Discrete vs.Continuous: Part B
    01:45
  - 7.06 Commonly Used Discrete Probability Distributions: Part A
    03:18
  - 7.07 Discrete Probability Distributions: Poisson
    03:16
  - 7.08 Binomial by Poisson Theorem
    02:28
  - 7.09 Commonly Used Continuous Probability Distribution
    03:22
  - 7.10 Application of Normal Distribution
    02:49
  - 7.11 Recap
    01:25
- Lesson 08: Sampling and Sampling Techniques
  36:45Preview
  - 8.01 Learnning Objectives
    00:51
  - 8.02 Introduction to Sampling and Sampling Errors
    03:05
  - 8.03 Advantages and Disadvantages of Sampling
    01:31
  - 8.04 Probability Sampling Methods: Part A
    02:32
  - 8.05 Probability Sampling Methods: Part B
    02:27
  - 8.06 Non-Probability Sampling Methods: Part A
    01:42
  - 8.07 Non-Probability Sampling Methods: Part B
    01:25
  - 8.08 Uses of Probability Sampling and Non-Probability Sampling
    02:08
  - 8.09 Sampling
    01:08
  - 8.10 Probability Distribution
    02:53
  - 8.11 Theorem Five Point One
    00:52
  - 8.12 Center Limit Theorem
    02:14
  - 8.13 Sampling Stratified: Sampling Example
    04:35
  - 8.14 Probability Sampling: Example
    01:17
  - 8.15 Recap
    01:07
  - 8.16 Case Study Three: Sample and Sampling Techniques
    05:16
  - 8.17 Spotlight
    01:42
- Lesson 09: Inferential Statistics
  37:08Preview
  - 9.01 Learning Objectives
    01:04
  - 9.02 Inferential Statistics
    03:09
  - 9.03 Hypothesis and Hypothesis Testing in Businesses
    03:24
  - 9.04 Null and Alternate Hypothesis
    01:44
  - 9.05 P Value
    03:22
  - 9.06 Levels of Significance
    01:16
  - 9.07 Type One and Two Errors
    01:37
  - 9.08 Z Test
    02:24
  - 9.09 Confidence Intervals and Percentage Significance Level: Part A
    02:52
  - 9.10 Confidence Intervals: Part B
    01:20
  - 9.11 One Tail and Two Tail Tests
    04:43
  - 9.12 Notes to Remember for Null Hypothesis
    01:02
  - 9.13 Alternate Hypothesis
    01:51
  - 9.14 Recap
    00:56
  - 9.15 Case Study 4: Inferential Statistics
    06:24
  - Hypothesis Testing
- Lesson 10: Application of Inferential Statistics
  27:20Preview
  - 10.01 Learning Objectives
    00:50
  - 10.02 Bivariate Analysis
    02:01
  - 10.03 Selecting the Appropriate Test for EDA
    02:29
  - 10.04 Parametric vs. Non-Parametric Tests
    01:54
  - 10.05 Test of Significance
    01:38
  - 10.06 Z Test
    04:27
  - 10.07 T Test
    00:54
  - 10.08 Parametric Tests ANOVA
    03:26
  - 10.09 Chi-Square Test
    02:31
  - 10.10 Sign Test
    01:58
  - 10.11 Kruskal Wallis Test
    01:04
  - 10.12 Mann Whitney Wilcoxon Test
    01:18
  - 10.13 Run Test for Randomness
    01:53
  - 10.14 Recap
    00:57
- Lesson 11: Relation between Variables
  20:07Preview
  - 11.01 Learning Objectives
    01:06
  - 11.02 Correlation
    01:54
  - 11.03 Karl Pearson's Coefficient of Correlation
    02:36
  - 11.04 Karl Pearsons: Use Cases
    01:30
  - 11.05 Correlation Example
    01:59
  - 11.06 Spearmans Rank Correlation Coefficient
    02:14
  - 11.07 Causation
    01:47
  - 11.08 Example of Regression
    02:28
  - 11.09 Coefficient of Determination
    01:12
  - 11.10 Quantifying Quality
    02:29
  - 11.11 Recap
    00:52
- Lesson 12: Application of Statistics in Business
  17:25Preview
  - 12.01 Learning Objectives
    00:53
  - 12.02 How to Use Statistics In Day to Day Business
    03:29
  - 12.03 Example: How to Not Lie With Statistics
    02:34
  - 12.04 How to Not Lie With Statistics
    01:49
  - 12.05 Lying Through Visualizations
    02:15
  - 12.06 Lying About Relationships
    03:31
  - 12.07 Recap
    01:06
  - 12.08 Spotlight
    01:48
- Lesson 13: Assisted Practice
  11:47
  - Assisted Practice: Problem Statement
    02:10
  - Assisted Practice: Solution
    09:37

Programme Syllabus

View Schedules

Industry Projects

Project 1
Sales Analysis for Business Growth
Analyze the sales data of a retail clothing company and support the management in formulating their sales and growth strategy.
Project 2
Marketing Campaign Analysis
Perform exploratory data analysis and hypothesis testing to better understand the 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 the elements influencing house prices, and understand the impact of various house features on their price.
Project 5
Customer Behaviour Analysis
Utilize various probability distributions to analyze customer behaviors and store performance metrics using a custom dataset.

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Data Science with Python Exam & Certification

Who provides the certification and how long is it valid for?
When a candidate successfully finished the course Python for Data Science training in Saint Paul, Simplilearn awards a Python for Data Science course in Saint Paul certificate of completion that's industry-recognized and valid for a lifetime.
What do I need to unlock my Simplilearn certificate?
The online classroom or online self-learning are the two options available to complete the Python for Data Science training in Saint Paul course. To unlock the Simplilearn certificate for:

Online Classroom:
- Attend an entire batch of the Python for Data Science course in Saint Paul.
- Submit at least one completed project
Online Self-Learning:
- Complete 85% of the course
- Submit at least one completed project
Do you provide any practice tests as part of the Data Science Python Course?
We provide one practice test as a free part of the Python for Data Science training in Saint Paul . This test will help in preparation for the actual certification exam. Candidates can also try the Free Data Science with Python Practice Test to learn more about the type of tests that are part of the Python for Data Science course in Saint Paul curriculum.

Data Science with Python Course Reviews

Mushtaque Ansari
Senior Software Developer, Bangalore
I thoroughly enjoyed my Data Science with Python course at Simplilearn. Vaishali's adept teaching, blending theory with practical applications in live sessions, made complex concepts clear. Grateful for the enriching experience!
Brian
Program Manager (iGPM RBEI), Bangalore
The training for the data science with Python course was meticulously organized, led by a seasoned instructor proficient in practical application. The trainer handled responses and queries efficiently with a good amount of patience, ensuring a smooth learning experience.
Vignesh Manikandan
Bangalore
The data science with python course offered online was excellently structured, facilitating rapid learning. Vaishali's expertise and professionalism ensured each session was insightful. Grateful for the wealth of knowledge gained in such a short span.
Arvind Kumar
Technology Lead, Nagpur
Attending the data science with Python course was enriching. Vaishali, my instructor, adeptly led each session. All topics were explained with in-depth theory, real-time examples, and practical Python applications. Her teaching approach truly amplified the learning journey.
Darshan Gajjar
Gandhinagar
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
Patna
I completed this course at Simplilearn. The faculty, Prashanth Nair, was extremely knowledgeable, and the entire class appreciated his way of teaching. Simplilearn's 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
Pune
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
Pune
Simplilearn facilitates a brilliant platform to acquire new & relevant skills at ease. Well laid out course content and expert faculty ensure an excellent learning experience.
Dastagiri Durgam
Hyderabad
Incredible mentorship, and amazing and unique lectures. Simplilearn provides a great way to learn with self-paced videos and recordings of online sessions. Thanks, Simplilearn, for providing quality education.
Mukesh Pandey
Hyderabad
Simplilearn is 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, I suggest you sign up with Simplilearn. They offer classes in almost all disciplines.
Surendaran Baskaran
Coimbatore
I took this course with Simplilearn. 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!!
Shiv Sharma
Mumbai
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, Bangalore
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.
Rakshith Ramesh
Software Program Lead Engineer, Bangalore
I wanted to advance my career in the semiconductor industry, so I took a Data Science with Python course. Simplilearn's curriculum appealed to me, and my experience was enriching. The instructors were knowledgeable, and I learned both data science and Python. This certification led to a job at Lam Research with a 110% salary increase.
Satabdi Adhikary
Bangalore
Simplilearn's 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.

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Why Join this Program

Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Data Science with Python Training FAQs

What is the average salary of a data science with python professional in St Paul?
The average salary of a data scientist in the USA is $96,491/year. A data scientist in St Paul can expect to receive a salary of the same scale. Having a certificate in python for data science training St Paul course helps, along with prior experience.
What are the major companies hiring for data science with python professionals with python in St Paul?
Cognizant, TCS, General Mills, Colsh Consultants, and Medtronic are some big-shot companies in St Paul that hire data scientists. After completing the Python for data science training at St Paul, one will be ready for the industrial experience and recruited by companies like these.
What are the major companies hiring for data science with python professionals with python in St Paul?
Cognizant, TCS, General Mills, Colsh Consultants, and Medtronic are some big-shot companies in St Paul that hire data scientists. After completing the Python for data science training at St Paul, one will be ready for the industrial experience and recruited by companies like these.
What are the major industries in St Paul?
The major economic sectors in St Paul are manufacturing, services, wholesale, retail, and technology. The city has a high concentration of technology-related firms, so that one can find several opportunities here. With the python for data science training at St Paul, a professional can boost their chances of landing a job.
How to become a data scientist in St Paul?
A data scientist will be dealing with data in large amounts. So, an individual in this field will be expected to be experienced in working with big data and data analytic tools and techniques. Learning Python for data science training at St Paul can help in mastering all these skills.
How to find data science with Python training courses in St Paul?
St Paul is a great place to study any course or degree. It's an educational hub that houses several famous colleges and universities, and one can find courses in data science in these institutions. Special courses like python for data science training St Paul can be found on online platforms.
Who are the instructors for this data science with Python course, and how are they selected?
The instructors for this data science with Python course are industry experts with extensive experience in the field. They are selected based on expertise, industry recognition, and teaching ability to ensure you receive top-quality education and insights.

Data Science with Python Training in Saint Paul

The capital of the US state Minnesota, St Paul, hosted 308,096 people as of 2019. Most of the city lies at the confluence of the Mississippi and Minnesota rivers. It is a densely populated city.

The GDP of the St Paul-Minneapolis Metro area was estimated to be 242.5 billion USD in 2019. The GDP per capita of the state of Minnesota was $59,510, according to the data in 2018.

The city sports a continental climate that's not too harsh. St Paul is an excellent place for any individual looking to establish themselves in the technology field. This can be done by completing the python for data science training at St Paul.

Being the twin City of Minneapolis, St Paul hosts several landmarks and attractions of great entertainment. One can find many historical sites and buildings in this city. Apart from that, here are some must-visit sites in St Paul that are known for their history and significance:

Acknowledgement
PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, OPM3 and the PMI ATP seal are the registered marks of the Project Management Institute, Inc.

Data Science with Python Training in Saint Paul

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Self Paced Learning

online Bootcamp

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Data Science with Python Course Curriculum

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Course Content

Data Science with Python

Lesson 01 - Course Introduction

1.01 Course Introduction

1.02 What you will Learn

Lesson 02 - Introduction to Data Science

2.01 Introduction

2.02 Data Science and its Applications

2.03 The Data Science Process: Part 1

2.04 The Data Science Process: Part 2

2.05 Recap

Lesson 03 - Essentials of Python Programming

3.01 Introduction

3.02 Setting Up Jupyter Notebook: Part 1

3.03 Setting Up Jupyter Notebook: Part 2

3.04 Python Functions

3.05 Python Types and Sequences

3.06 Python Strings Deep Dive

3.07 Python Demo: Reading and Writing csv files

3.08 Date and Time in Python

3.09 Objects in Python Map

3.10 Lambda and List Comprehension

3.11 Why Python for Data Analysis?

3.12 Python Packages for Data Science

3.13 StatsModels Package: Part 1

3.14 StatsModels Package: Part 2

3.15 Scipy Package

3.16 Recap

3.17 Spotlight

Lesson 04 - NumPy

4.01 Introduction

4.02 Fundamentals of NumPy

4.03 Array shapes and axes in NumPy: Part A

4.04 NumPy Array Shapes and Axes: Part B

4.05 Arithmetic Operations

4.06 Conditional Logic

4.07 Common Mathematical and Statistical Functions in Numpy

4.08 Indexing And Slicing: Part 1

4.09 Indexing and Slicing: Part 2

4.10 File Handling

4.11 Recap

Lesson 05 - Linear Algebra

5.01 Introduction

5.02 Introduction to Linear Algebra

5.03 Scalars and Vectors

5.04 Dot Product of Two Vectors

5.05 Linear independence of Vectors

5.06 Norm of a Vector

5.07 Matrix

5.08 Matrix Operations

5.09 Transpose of a Matrix

5.10 Rank of a Matrix

5.11 Determinant of a matrix and Identity matrix or operator

5.12 Inverse of a matrix and Eigenvalues and Eigenvectors

5.13 Calculus in Linear Algebra

5.14 Recap

Lesson 06 - Statistics Fundamentals

6.01 Introduction

6.02 Importance of Statistics with Respect to Data Science

6.03 Common Statistical Terms

6.04 Types of Statistics

6.05 Data Categorization and Types

6.06 Levels of Measurement

6.07 Measures of Central Tendency

6.08 Measures of Central Tendency