Friday 9 August 2024
Sunday 23 June 2024
Demonstrating different types of colormaps
Python Coding June 23, 2024 Data Science, Python No comments
import matplotlib.pyplot as plt
import numpy as np
# Generate sample data
data = np.random.rand(10, 10)
# List of colormaps to demonstrate
colormaps = [
'viridis', # Sequential
'plasma', # Sequential
'inferno', # Sequential
'magma', # Sequential
'cividis', # Sequential
'PiYG', # Diverging
'PRGn', # Diverging
'BrBG', # Diverging
'PuOr', # Diverging
'Set1', # Qualitative
'Set2', # Qualitative
'tab20', # Qualitative
'hsv', # Cyclic
'twilight', # Cyclic
'twilight_shifted' # Cyclic
]
# Create subplots to display colormaps
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))
# Flatten axes array for easy iteration
axes = axes.flatten()
# Loop through colormaps and plot data
for ax, cmap in zip(axes, colormaps):
im = ax.imshow(data, cmap=cmap)
ax.set_title(cmap)
plt.colorbar(im, ax=ax)
# Adjust layout to prevent overlap
plt.tight_layout()
# Show the plot
plt.show()
Explanation:
Generate Sample Data:
data = np.random.rand(10, 10)This creates a 10x10 array of random numbers.
List of Colormaps:
 A list of colormap names is defined. Each name corresponds to a different colormap in Matplotlib.
Create Subplots:
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))This creates a 5x3 grid of subplots to display multiple colormaps.
Loop Through Colormaps:
 The loop iterates through each colormap, applying it to the sample data and plotting it in a subplot.
Add Colorbar:
plt.colorbar(im, ax=ax)
This adds a colorbar to each subplot to show the mapping of data values to colors.
Adjust Layout and Show Plot:
plt.tight_layout() plt.show()These commands adjust the layout to prevent overlap and display the plot.
Choosing Colormaps
 Sequential: Good for data with a clear order or progression.
 Diverging: Best for data with a critical midpoint.
 Qualitative: Suitable for categorical data.
 Cyclic: Ideal for data that wraps around, such as angles.
By selecting appropriate colormaps, you can enhance the visual representation of your data, making it easier to understand and interpret.
Friday 21 June 2024
Matrix in Python
Python Coding June 21, 2024 Data Science, Python No comments
Wednesday 12 June 2024
Data Science Basics to Advance Course Syllabus
Python Coding June 12, 2024 Data Science No comments
Week 1: Introduction to Data Science and Python Programming
 Overview of Data Science
 Understanding what data science is and its importance.
 Python Basics
 Introduction to Python, installation, setting up the development environment.
 Basic Python Syntax
 Variables, data types, operators, expressions.
 Control Flow
 Conditional statements, loops.
 Functions and Modules
 Defining, calling, and importing functions and modules.
 Handson Exercises
 Basic Python programs and assignments.
Week 2: Data Structures and File Handling in Python
 Data Structures
 Lists, tuples, dictionaries, sets.
 Manipulating Data Structures
 Indexing, slicing, operations.
 File Handling
 Reading from and writing to files, file operations.
 Error Handling
 Using tryexcept blocks.
 Practice Problems
 Miniprojects involving data structures and file handling.
Week 3: Data Wrangling with Pandas
 Introduction to Pandas
 Series and DataFrame objects.
 Data Manipulation
 Indexing, selecting data, filtering.
 Data Cleaning
 Handling missing values, data transformations.
 Data Integration
 Merging, joining, concatenating DataFrames.
 Handson Exercises
 Data wrangling with real datasets.
Week 4: Data Visualization
 Introduction to Matplotlib
 Basic plotting, customization.
 Advanced Visualization with Seaborn
 Statistical plots, customization.
 Interactive Visualization with Plotly
 Creating interactive plots.
 Data Visualization Projects
 Creating visualizations for real datasets.
Week 5: Exploratory Data Analysis (EDA)  Part 1
 Importance of EDA
 Understanding data and deriving insights.
 Descriptive Statistics
 Summary statistics, data distributions.
 Visualization for EDA
 Histograms, box plots.
 Correlation Analysis
 Finding relationships between variables.
 Handson Projects
 Conducting EDA on realworld datasets.
Week 6: Exploratory Data Analysis (EDA)  Part 2
 Visualization for EDA
 Scatter plots, pair plots.
 Handling Missing Values and Outliers
 Techniques for dealing with incomplete data.
 Feature Engineering
 Creating new features, transforming existing features.
 Handson Projects
 Advanced EDA techniques on real datasets.
Week 7: Data Collection and Preprocessing Techniques
 Data Collection Methods
 Surveys, web scraping, APIs.
 Data Cleaning
 Handling missing data, outliers, and inconsistencies.
 Data Transformation
 Normalization, standardization, encoding categorical variables.
 Handson Projects
 Collecting and preprocessing realworld data.
Week 8: Database Management and SQL
 Introduction to Databases
 Relational databases, database design.
 SQL Basics
 SELECT, INSERT, UPDATE, DELETE statements.
 Advanced SQL
 Joins, subqueries, window functions.
 Connecting Python to Databases
 Using libraries like SQLAlchemy.
 Handson Exercises
 SQL queries and database management projects.
Week 9: Introduction to Time Series Analysis
 Time Series Concepts
 Understanding time series data, components of time series.
 Time Series Visualization
 Plotting time series data, identifying patterns.
 Basic Time Series Analysis
 Moving averages, smoothing techniques.
 Handson Exercises
 Working with time series data.
Week 10: Advanced Time Series Analysis
 Decomposition
 Breaking down time series into trend, seasonality, and residuals.
 Forecasting Methods
 Introduction to ARIMA and other forecasting models.
 Model Evaluation
 Assessing forecast accuracy.
 Practical Application
 Time series forecasting projects.
Week 11: Advanced Data Wrangling with Pandas
 Advanced Data Manipulation
 Pivot tables, groupby operations.
 Time Series Manipulation
 Working with date and time data in Pandas.
 Merging and Joining DataFrames
 Advanced techniques for combining datasets.
 Practical Exercises
 Complex data wrangling tasks.
Week 12: Advanced Data Visualization Techniques
 Interactive Dashboards
 Creating dashboards with Dash and Tableau.
 Geospatial Data Visualization
 Mapping data with libraries like Folium.
 Storytelling with Data
 Effective communication of data insights.
 Practical Projects
 Building interactive and compelling data visualizations.
Monday 20 May 2024
Box and Whisker plot using Python Libraries
Python Coding May 20, 2024 Data Science, Python No comments
Step 1: Install Necessary Libraries
Step 2: Import Libraries
Step 3: Create Sample Data
Step 4: Create the Box and Whisker Plot
Step 5: Enhance the Plot with Seaborn
For more advanced styling, you can use seaborn, which provides more aesthetic options.
# Set the style of the visualization
sns.set(style="whitegrid")
# Create a boxplot with seaborn
plt.figure(figsize=(10, 6))
sns.boxplot(data=data)
# Add title and labels
plt.title('Box and Whisker Plot')
plt.xlabel('Category')
plt.ylabel('Values')
# Show plot
plt.show()
Sunday 5 May 2024
Donut Charts using Python
Python Coding May 05, 2024 Data Science, Python No comments
Code:
Explanation:
Code:
Explanation:
Code:
Explanation:
Saturday 4 May 2024
Data Science: The Hard Parts: Techniques for Excelling at Data Science
Python Coding May 04, 2024 Books, Data Science No comments
This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a notsogreat one.
Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.
With this book, you will:
Understand how data science creates value
Deliver compelling narratives to sell your data science project
Build a business case using unit economics principles
Create new features for a ML model using storytelling
Learn how to decompose KPIs
Perform growth decompositions to find root causes for changes in a metric
Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).
PDF: Data Science: The Hard Parts: Techniques for Excelling at Data Science
Hard Copy: Data Science: The Hard Parts: Techniques for Excelling at Data Science
Streamgraphs using Python
Python Coding May 04, 2024 Data Science No comments
Code:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
plt.stackplot(x, y1, y2, baseline='wiggle')
plt.title('Streamgraph')
plt.show()
Explanation:
Statistical Inference and Probability
Python Coding May 04, 2024 Books, Data Science No comments
An experienced author in the field of data analytics and statistics, John Macinnes has produced a straightforward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples. It covers a range of topics, including:
· Probability and Sampling distributions
· Inference and regression
· Power, effect size and inverse probability
Part of The SAGE Quantitative Research Kit, this book will give you the knowhow and confidence needed to succeed on your quantitative research journey.
Hard Copy: Statistical Inference and Probability
PDF: Statistical Inference and Probability (The SAGE Quantitative Research Kit)
Friday 26 April 2024
Top 4 free Mathematics course for Data Science !
Python Coding April 26, 2024 Data Science No comments
In the age of big data, understanding statistics and data science concepts is becoming increasingly crucial across various industries. From finance to healthcare, businesses are leveraging datadriven insights to make informed decisions and gain a competitive edge. In this blog post, we'll embark on a journey through fundamental statistical concepts, explore the powerful technique of KMeans Clustering in Python, delve into the realm of probability, and demystify practical time series analysis.
In our tutorial, we'll walk through the implementation of KMeans clustering using Python, focusing on the following steps:
Understanding the intuition behind KMeans clustering.Preprocessing the data and feature scaling.Choosing the optimal number of clusters using techniques like the Elbow Method or Silhouette Score.Implementing KMeans clustering using scikitlearn.Visualizing the clustering results to gain insights into the underlying structure of the data.
Probability theory is the mathematical framework for analyzing random phenomena and quantifying uncertainty. Whether you're predicting the outcome of a coin toss or estimating the likelihood of a stock market event, probability theory provides the tools to make informed decisions in the face of uncertainty.
In this section, we'll provide an intuitive introduction to probability, covering essential concepts such as:
Basic probability terminology: events, sample space, and outcomes.Probability axioms and rules: addition rule, multiplication rule, and conditional probability.Probability distributions: discrete and continuous distributions.Common probability distributions: Bernoulli, binomial, normal, and Poisson distributions.Applications of probability theory in realworld scenarios.
Practical Time Series Analysis
Python Coding April 26, 2024 Course, Coursera, Data Science No comments
There are 6 modules in this course
Welcome to Practical Time Series Analysis!
Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.
In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.
Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.
You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!
Join Free: Practical Time Series Analysis
Time Series Analysis can take effort to learn we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
Thursday 18 April 2024
Meta Data Analyst Professional Certificate
Python Coding April 18, 2024 Data Science No comments
Why Take a Meta Data Analyst Professional Certificate?
Collect, clean, sort, evaluate, and visualize data
Apply the Obtain, Sort, Explore, Model, Interpret (OSEMN) framework to guide the data analysis process
Learn to use statistical analysis, including hypothesis testing, regression analysis, and more, to make datadriven decisions
Develop an understanding of the foundational principles underpinning effective data management and usability of data assets within organizational context
Aquire the confidence to add the following skills to add to your resume:
Data analysis
Python Programming
Statistics
Data management
Datadriven decision making
Data visualization
Linear Regression
Hypothesis testing
Data Management
Tableau
Join Free: Meta Data Analyst Professional Certificate
What you'll learn
Collect, clean, sort, evaluate, and visualize data
Apply the OSEMN, framework to guide the data analysis process, ensuring a comprehensive and structured approach to deriving actionable insights
Use statistical analysis, including hypothesis testing, regression analysis, and more, to make datadriven decisions
Develop an understanding of the foundational principles of effective data management and usability of data assets within organizational context
Professional Certificate  5 course series
Prepare for a career in the highgrowth field of data analytics. In this program, you’ll build indemand technical skills like Python, Statistics, and SQL in spreadsheets to get jobready in 5 months or less, no prior experience needed.
Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decisionmaking and strategy across an organization.
In this program, you’ll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You’ll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data.
You’ll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results.
When you complete this full program, you’ll have a portfolio of handson projects and a Professional Certificate from Meta to showcase your expertise.
Applied Learning Project
Throughout the program, you’ll get to practice your new data analysis skills through handson projects including:
Identifying data sources
Using spreadsheets to clean and filter data
Using Python to sort and explore data
Using Tableau to visualize results
Using statistical analyses
By the end, you’ll have a professional portfolio that you can show to prospective employers or utilize for your own business.
Tuesday 16 April 2024
do you know difference between Data Analyst , Data Scientist and Data Engineer?
Python Coding April 16, 2024 Data Science No comments
Data Analyst
A data analyst sits between business intelligence and data science. They provide vital information to business stakeholders.
Data Management in SQL (PostgreSQL)
Data Analysis in SQL (PostgreSQL)
Exploratory Analysis Theory
Statistical Experimentation Theory
Free Certification : Data Analyst Certification
Data Scientist Associate
A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data.
R / Python Programming
Data Manipulation in R/Python
1.1 Calculate metrics to effectively report characteristics of data and relationships between
features
● Calculate measures of center (e.g. mean, median, mode) for variables using R or Python.
● Calculate measures of spread (e.g. range, standard deviation, variance) for variables
using R or Python.
● Calculate skewness for variables using R or Python.
● Calculate missingness for variables and explain its influence on reporting characteristics
of data and relationships in R or Python.
● Calculate the correlation between variables using R or Python.
1.2 Create data visualizations in coding language to demonstrate the characteristics of data
● Create and customize bar charts using R or Python.
● Create and customize box plots using R or Python.
● Create and customize line graphs using R or Python.
● Create and customize histograms graph using R or Python.
1.3 Create data visualizations in coding language to represent the relationships between
features
● Create and customize scatterplots using R or Python.
● Create and customize heatmaps using R or Python.
● Create and customize pivot tables using R or Python.
1.4 Identify and reduce the impact of characteristics of data
● Identify when imputation methods should be used and implement them to reduce the
impact of missing data on analysis or modeling using R or Python.
● Describe when a transformation to a variable is required and implement corresponding
transformations using R or Python.
● Describe the differences between types of missingness and identify relevant approaches
to handling types of missingness.
● Identify and handle outliers using R or Python.
Statistical Fundamentals in R/Python
2.1 Perform standard data import, joining and aggregation tasks
● Import data from flat files into R or Python.
● Import data from databases into R or Python
● Aggregate numeric, categorical variables and dates by groups using R or Python.
● Combine multiple tables by rows or columns using R or Python.
● Filter data based on different criteria using R or Python.
2.2 Perform standard cleaning tasks to prepare data for analysis
● Match strings in a dataset with specific patterns using R or Python.
● Convert values between data types in R or Python.
● Clean categorical and text data by manipulating strings in R or Python.
● Clean date and time data in R or Python.
2.3 Assess data quality and perform validation tasks
● Identify and replace missing values using R or Python.
● Perform different types of data validation tasks (e.g. consistency, constraints, range
validation, uniqueness) using R or Python.
● Identify and validate data types in a data set using R or Python.
2.4 Collect data from nonstandard formats by modifying existing code
● Adapt provided code to import data from an API using R or Python.
● Identify the structure of HTML and JSON data and parse them into a usable format for
data processing and analysis using R or Python
Importing & Cleaning in R/Python
Machine Learning Fundamentals in R/Python
Free Certification : Data Science
Data Engineer
A data engineer collects, stores, and preprocesses data for easy access and use within an organization. Associate certification is available.
Data Management in SQL (PostgreSQL)
Exploratory Analysis Theory
Free Certification : Data Science
Sunday 14 April 2024
4 Free books to master Data Analytics
Python Coding April 14, 2024 Data Science No comments
Storytelling with Data: A Data Visualization Guide for Business Professionals
Don't simply show your data  tell a story with it!
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous realworld examples  ready for immediate application to your next graph or presentation.
Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:
Understand the importance of context and audience
Determine the appropriate type of graph for your situation
Recognize and eliminate the clutter clouding your information
Direct your audience's attention to the most important parts of your data
Think like a designer and utilize concepts of design in data visualization
Leverage the power of storytelling to help your message resonate with your audience
Together, the lessons in this book will help you turn your data into highimpact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data  Storytelling with Data will give you the skills and power to tell it!
Fundamentals of Data Analytics: Learn Essential Skills, Embrace the Future, and Catapult Your Career in the DataDriven World—A Comprehensive Guide to Data Literacy for Beginners
Gain a competitive edge in today’s datadriven world and build a rich career as a data professional that drives business success and innovation…
Today, data is everywhere… and it has become the essential building block of this modern society.
And that’s why now is the perfect time to pursue a career in data.
But what does it take to become a competent data professional?
This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving realworld datarelated problems.
Here is just a fraction of what you will discover:
A beginnerfriendly 5step framework to kickstart your journey into analyzing and processing data
How to get started with the fundamental concepts, theories, and models for accurately analyzing data
Everything you ever needed to know about data mining and machine learning principles
Why business run on a datadriven culture, and how you can leverage it using realtime business intelligence analytics
Strategies and techniques to build a problemsolving mindset that can overcome any complex and unique dataset
How to create compelling and dynamic visualizations that help generate insights and make datadriven decisions
The 4 pillars of a new digital world that will transform the landscape of analyzing data
And much more.
Believe it or not, you can be terrible in math or statistics and still pursue a career in data.
And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you.
Ready to master the fundamentals and build a successful career in data analytics? Click the “Add to Cart” button right now.
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: Python for Data Science, Book 2
Data Analytics, Data Visualization & Communicating Data: 3 books in 1: Learn the Processes of Data Analytics and Data Science, Create Engaging Data Visualizations, and Present Data Effectively
Harvard Business Review called data science “the sexiest job of the 21st century,” so it's no surprise that data science jobs have grown up to 20 times in the last three years. With demand outpacing supply, companies are willing to pay top dollar for talented data professionals. However, to stand out in one of these positions, having foundational knowledge of interpreting data is essential. You can be a spreadsheet guru, but without the ability to turn raw data into valuable insights, the data will render useless. That leads us to data analytics and visualization, the ability to examine data sets, draw meaningful conclusions and trends, and present those findings to the decisionmaker effectively.
Mastering this skill will undoubtedly lead to better and faster business decisions. The three audiobooks in this series will cover the foundational knowledge of data analytics, data visualization, and presenting data, so you can master this essential skill in no time. This series includes:
Everything data analytics: a beginner's guide to data literacy and understanding the processes that turns data into insights.
Beginner's guide to data visualization: how to understand, design, and optimize over 40 different charts.
How to win with your data visualizations: the five part guide for junior analysts to create effective data visualizations and engaging data stories.
These three audiobooks cover an extensive amount of information, such as:
Overview of the data collection, management, and storage processes.
Fundamentals of cleaning data.
Essential machine learning algorithms required for analysis such as regression, clustering, classification, and more....
The fundamentals of data visualization.
An indepth view of over 40 plus charts and when to use them.
A comprehensive data visualization design guide.
Walkthrough on how to present data effectively.
And so much more!
Tuesday 2 April 2024
Doughnut Plot using Python
Python Coding April 02, 2024 Data Science, Python No comments
import plotly.graph_objects as go
# Sample data
labels = ['A', 'B', 'C', 'D']
values = [20, 30, 40, 10]
colors = ['#FFA07A', '#FFD700', '#6495ED', '#ADFF2F']
# Create doughnut plot
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5, marker=dict(colors=colors))])
fig.update_traces(textinfo='percent+label', textfont_size=14, hoverinfo='label+percent')
fig.update_layout(title_text="Customized Doughnut Plot", showlegend=False)
# Show plot
fig.show()
#clcoding.com
import matplotlib.pyplot as plt
# Sample data
labels = ['Category A', 'Category B', 'Category C', 'Category D']
sizes = [20, 30, 40, 10]
explode = (0, 0.1, 0, 0) # "explode" the 2nd slice
# Create doughnut plot
fig, ax = plt.subplots()
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=90, shadow=True, colors=plt.cm.tab20.colors)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
# Draw a white circle at the center to create a doughnut plot
centre_circle = plt.Circle((0, 0), 0.7, color='white', fc='white', linewidth=1.25)
fig.gca().add_artist(centre_circle)
# Add a title
plt.title('Doughnut Plot with Exploded Segment and Shadow Effect')
# Show plot
plt.show()
#clcoding.com
import plotly.graph_objects as go
# Sample data
labels = ['A', 'B', 'C', 'D']
values = [20, 30, 40, 10]
# Create doughnut plot
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5)])
fig.update_layout(title_text="Doughnut Plot")
# Show plot
fig.show()
#clcoding.com
import matplotlib.pyplot as plt
# Sample data
labels = ['Category A', 'Category B', 'Category C', 'Category D']
sizes = [20, 30, 40, 10]
# Create doughnut plot
fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=plt.cm.tab20.colors)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
# Draw a white circle at the center to create a doughnut plot
centre_circle = plt.Circle((0, 0), 0.7, color='white', fc='white', linewidth=1.25)
fig.gca().add_artist(centre_circle)
# Add a title
plt.title('Doughnut Plot')
# Show plot
plt.show()
#clcoding.com
Friday 8 March 2024
Fractal Data Science Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science No comments
What you'll learn
Apply structured problemsolving techniques to dissect and address complex datarelated challenges encountered in realworld scenarios.
Utilize SQL proficiency to retrieve, manipulate data and employ data visualization skills using Power BI to communicate insights.
Apply Python expertise for data manipulation, analysis and implement machine learning algorithms to create predictive models for applications.
Create compelling data stories to influence your audience and master the art of critically analyzing data while making decisions and recommendations.
Join Free: Fractal Data Science Professional Certificate
Professional Certificate  8 course series
CertNexus Certified Data Science Practitioner Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science No comments
Advance your career with indemand skills
Receive professionallevel training from CertNexus
Demonstrate your technical proficiency
Earn an employerrecognized certificate from CertNexus
Prepare for an industry certification exam
Join Free: CertNexus Certified Data Science Practitioner Professional Certificate
Professional Certificate  5 course series
IBM Data Engineering Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science, IBM No comments
What you'll learn
Master the most uptodate practical skills and knowledge data engineers use in their daily roles
Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2
Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming
Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards
Join Free: IBM Data Engineering Professional Certificate
Professional Certificate  13 course series
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