Data Science with Python Training in Jersey City, NJ

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Data Science with Python Course Overview

The Python for Data Science Training in Jersey City course will help you apply Data Science methods and acquire essential data analysis skills and concepts related to Python programming. The Python for Data Science Course in Jersey City teaches you concepts like NLP, Machine Learning (ML), Web Scraping, Data Analysis, and Data Visualization.

Data Science with Python Training Key Features

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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!
  • 68 hours of blended learning
  • 40+ Assisted practices and lesson-wise knowledge checks
  • Dedicated live sessions by faculty of industry experts
  • Industry-based projects for experiential learning
  • Lifetime access to self-paced learning content
  • Practical skills and hands-on experience in applying Python to address data science challenges.
  • 68 hours of blended learning
  • Industry-based projects for experiential learning
  • 40+ Assisted practices and lesson-wise knowledge checks
  • Lifetime access to self-paced learning content
  • Dedicated live sessions by faculty of industry experts
  • Practical skills and hands-on experience in applying Python to address data science challenges.
  • 68 hours of blended learning
  • Industry-based projects for experiential learning
  • 40+ Assisted practices and lesson-wise knowledge checks
  • Lifetime access to self-paced learning content
  • Dedicated live sessions by faculty of industry experts
  • Practical skills and hands-on experience in applying Python to address data science challenges.

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

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Benefits

The Python for Data Science Training in Jersey City 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
  • Annual Salary
    $43KMin
    $62KAverage
    $95KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Jersey City
    JPMorgan Chase hiring for Data Analyst professionals in Jersey City
    Genpact hiring for Data Analyst professionals in Jersey City
    VMware hiring for Data Analyst professionals in Jersey City
    LarsenAndTurbo hiring for Data Analyst professionals in Jersey City
    Citi hiring for Data Analyst professionals in Jersey City
    Accenture hiring for Data Analyst professionals in Jersey City
    Source: Indeed
  • Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Jersey City
    Oracle hiring for Data Scientist professionals in Jersey City
    Microsoft hiring for Data Scientist professionals in Jersey City
    Walmart hiring for Data Scientist professionals in Jersey City
    Amazon hiring for Data Scientist professionals in Jersey City
    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

$799

online Bootcamp

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Cohorts starting in Jersey City from:
5th Apr: Weekday Class
8th Apr: Weekday Class
View all cohorts

$1,299

Corporate Training

Customised to enterprise needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Data Science with Python Course Curriculum

Eligibility

The Python for Data Science training in Jersey City 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.
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Pre-requisites

The Python for Data Science course in Jersey City 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.
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Course Content

  • Data Science with Python

    Preview
    • Lesson 01 - Course Introduction

      04:59Preview
      • 1.01 Course Introduction
        03:06
      • 1.02 What you will Learn
        01:53
    • Lesson 02 - Introduction to Data Science

      08:16Preview
      • 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

      01:00:18Preview
      • 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

      28:19Preview
      • 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

      28:29Preview
      • 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

      33:50Preview
      • 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

      30:18Preview
      • 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

      01:07:21Preview
      • 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

      41:13Preview
      • 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

      32:25Preview
      • 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

      34:24Preview
      • 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

      42:55Preview
      • 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

      35:34Preview
      • 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
  • Math Refresher

    Preview
    • Lesson 01: Course Introduction

      06:23Preview
      • 1.01 About Simplilearn
        00:28
      • 1.02 Introduction to Mathematics
        01:18
      • 1.03 Types of Mathematics
        02:39
      • 1.04 Applications of Math in Data Industry
        01:17
      • 1.05 Learning Path
        00:25
      • 1.06 Course Components
        00:16
    • Lesson 02: Probability and Statistics

      32:38Preview
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check
    • Lesson 03: Coordinate Geometry

      06:31
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check
    • Lesson 04: Linear Algebra

      29:53Preview
      • 4.01 Learning Objectives
        00:29
      • 4.02 Introduction to Linear Algebra
        03:21
      • 4.03 Forms of Linear Equation
        05:21
      • 4.04 Solving a Linear Equation
        05:21
      • 4.05 Introduction to Matrices
        02:05
      • 4.06 Matrix Operations
        07:07
      • 4.07 Introduction to Vectors
        01:00
      • 4.08 Types and Properties of Vectors
        01:52
      • 4.09 Vector Operations
        02:39
      • 4.10 Key Takeaways
        00:38
      • 4.11 Knowledge Check
    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      08:56
      • 5.01 Learning Objectives
        00:29
      • 5.02 Eigenvalues
        01:19
      • 5.03 Eigenvectors
        04:09
      • 5.04 Eigendecomposition
        02:21
      • 5.05 Key Takeaways
        00:38
      • 5.06 Knowledge Check
    • Lesson 06: Introduction to Calculus

      09:47Preview
      • 6.01 Learning Objectives
        00:30
      • 6.02 Basics of Calculus
        01:20
      • 6.03 Differential Calculus
        03:01
      • 6.04 Differential Formulas
        01:01
      • 6.05 Integral Calculus
        02:33
      • 6.06 Integration Formulas
        00:47
      • 6.07 Key Takeaways
        00:35
      • 6.08 Knowledge Check
  • 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

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

Data Science with Python Training in Jersey City, NJ
  • Who provides the certification and how long is it valid for?

    When a candidate successfully finished the course Python for Data Science training in Jersey City, Simplilearn awards a Python for Data Science course in Jersey City 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 Jersey City course. To unlock the Simplilearn certificate for:

    Online Classroom:

    • Attend an entire batch of the Python for Data Science course in Jersey City. 
    • 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 Jersey City . 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 Jersey City curriculum.

Data Science with Python Course Reviews

  • Vignesh Manikandan

    Vignesh Manikandan

    Bangalore

    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.

  • Arvind Kumar

    Arvind Kumar

    Technology Lead, Nagpur

    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.

  • Mushtaque Ansari

    Mushtaque Ansari

    Senior Software Developer, Bangalore

    I had a wonderful experience learning Data Science with Python with Simplilearn. Thank you, Vaishali for explaining concepts theoretically and practically. The live sessions helped me easily understand the concepts.

  • Brian

    Brian

    Program Manager (iGPM RBEI), Bangalore

    The training was well-structured, and the trainer was experienced with hands-on know-how. The trainer handled responses and queries efficiently with a good amount of patience.

  • Darshan Gajjar

    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

    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

    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.

  • Mukesh Pandey

    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.

  • C Muthu Raman

    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

    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.

  • Surendaran Baskaran

    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!!

  • Akash Raj

    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.

  • Shiv Sharma

    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.

  • Kiran Kumar

    Kiran Kumar

    Bangalore

    I recently enrolled in the Data Scientist Master’s Program at Simplilearn. The syllabus is systematically structured, and the Live sessions are explained with real-time examples. This makes the course more accessible to freshers with basic knowledge. Looking forward to completing it. Thanks, Simplilearn Team.

  • Satabdi Adhikary

    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 Online Bootcamp

  • 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 salary of a Python Expert in Jersey?

    In Jersey City, NJ, the average salary for a Python developer is $69,892 per year, with a $5,000 cash incentive, making Python among the best-paid programming languages, after Ruby and C++.

  • What are the major companies hiring for a Python expert in Jersey?

    For someone who is familiar with the Python programming language, becoming a Python developer is the most direct path. The following are the top companies in Jersey that are hiring Python Experts: Selby Jennings, Enterprise Peak, Centene, Vaco, IDC Technologies, New York Technology Partners.

  • What are the major industries in Jersey?

    Some of the main industries in New Jersey are: Life Sciences Industry, Technology industry with major technology subsectors in NJ as Artificial intelligence, big data, telecommunications, cybersecurity, and fintech. Other industries include, clean energy, advanced Manufacturing Industry, logistics industry, Food and Beverage Industry, film industry, and many more.

  • How to become a Python expert in Jersey?

    By completing the extensive course in python programming that incorporates many abilities to be a python developer, you can become a python expert. There are no requirements for this course, but some previous understanding of the programming language is desirable. Regardless of whether you're new to programming or wish to learn new languages, a Python course teaches you Python's insights and outs, such as building Python scripts, sequence and file operations, object-oriented principles and web scraping.

  • How to find a Python expert training in Jersey?

    You can search online for Python Expert Training e-learning courses in Jersey. The best possible learning experience with flexible time frames is provided by an e-learning course. Choose a course that offers the best training modules, practical experiences, extended assistance, etc.

  • What is the importance of learning Python for Data Science?

    Python is the most popular programming language for Data Science. Python is widely used to perform data analysis, data manipulation, and data visualization. The advantages of using Python for data science are:

    • Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications in Data Science.
    • Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.
    • Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development in Data Science.

    When learning about Data Science with Python, you will gain a clear understanding of Python topics like functions, classes, lists, dictionaries, sets, tuples, and various Python libraries. Further, you will go through concepts like mathematical computing, data visualization, data exploration, data analysis, web scraping, machine learning, and feature engineering.

  • Do I need coding experience to learn Python for Data Science?

    If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python for Data Science. However, it is not compulsory. Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.

  • I have familiarity in other programming languages like C++/Java. Will the Data Science with Python course help me to switch to Python?

    Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.

  • What are the system requirements to install Python for Data Science?

    To run Python, your system must fulfill the following basic requirements:

    • 32 or 64-bit Operating System
    • 1GB RAM 

    The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instructions on how to install them.

  • Who are our instructors and how are they selected?

    All of our highly qualified Data Science trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What are the modes of training offered for this Python Data Science course?

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the Python Data Science course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.
     
    Online Self-Learning: In this mode, you will receive lecture videos and can proceed through the course at your convenience.
     
    WinPython portable distribution is the open-source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training.

  • What if I miss a class?

    Simplilearn provides recordings of each class so you can review them as needed before the next session.

  • Can I cancel my enrollment? Will I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about group discounts.

  • Whom should I contact to learn more about this Python Data Science course?

    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in Data Science on your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Python Data Science course with us.
    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

  • Disclaimer

    The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Data Science with Python Training in Jersey City, NJ

Jersey, officially the Bailiwick of Jersey, is a self-governing British Crown Dependency located off the coast of north-western France. Jersey is a 118.2-square-kilometer island on the English Channel. With a maximum land elevation of 143 m (469 ft) above sea level, it is the largest and southernmost of the Channel Islands and part of the British Isles. The total resident population was predicted to be 97,857 people according to the 2011 census, of whom 34% live in Saint Helier, the island's only town. The people of Jersey are often called Islanders or, in individual terms, Jerseyman or Jersey-woman.

Although New Jersey is the 4th smallest state in the country, it is also the most densely populated. Top attractions in jersey are:

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