September 26, 2022 
5 MIN READ 
Taking the Fear Out: My Experience as a Woman in Data Science
When I started my journey after taking a data science course, I was absolutely terrified. I was convinced that I would be surrounded by men who knew more than me and that I would be judged for being a woman in data science.
As it turns out, I couldn’t have been more wrong.
Today, I’ll share my experience as a woman in data science and how you can succeed too!
Why Data Science?
I originally got interested in data science because of my background in math and statistics. I loved finding patterns and problem-solving, so data science seemed like a natural fit.
After doing some research, I realized that data science is one of the most in-demand fields right now and that the opportunities are endless.
Despite my initial nerves, I decided to go for a data science course!
My Experience as a Woman in Data Science
As it turns out, there are quite a few women who have completed a data science course and then pursued in this field!
In my experience, the data science community has been incredibly welcoming and supportive. I’ve never felt like my gender has been a disadvantage; if anything, it’s been an asset.
Being a woman in data science has opened up opportunities for me to network with other women in this field and learn from their experiences. It’s also given me a chance to mentor other women who are just starting out their data science course.
How You Can Succeed in Data Science Too!
If you’re thinking about taking a data science course or pursuing a career in data science but you’re feeling intimidated or unsure of where to start, here are some tips:
- Do your research: There are tons of resources available online and offline for a data science course. Read articles, listen to podcasts, and watch videos to get an idea of what data science is all about.
- Find a mentor: Finding someone who is already established in data science can give you an idea of what your journey might look like and help you avoid common mistakes. If you don’t know anyone personally, reach out to your local meetup groups or online forums.
- Believe in yourself: This might seem like an obvious advice, but it’s worth repeating! Pursuing any career can be daunting, but remember that you have the power to control your own narrative. When it comes to succeeding in data science, confidence is key!
Decoding Data Science
Blog Introduction: Data science can seem like an intimidating field, but it doesn’t have to be! This three-part series will introduce you to the basics of data science and give you the tools you need to get started in this exciting field.
Part 1: What is Data Science?
Data science is a relatively new field that combines aspects of statistics, computer science, and domain expertise to gain insights from data.
Data science practitioners use a variety of techniques to wrangle, analyze, and visualize data so that it can be used to solve problems in business, government, and academia.
The term “data science” is often used interchangeably with “data analytics” or “big data,” but there are some important distinctions between these fields.
Data analytics focuses on descriptive analysis, which means understanding what has happened in the past.
In contrast, data science encompasses both predictive and prescriptive analysis, which means using data to predict what will happen in the future and prescribe what should be done to achieved a desired outcome.
Data science is sometimes also confused with machine learning, which is a subset of artificial intelligence that deals with algorithms that learn from data.
However, machine learning is just one tool that data scientists use to analyze data; other important tools include statistical analysis and exploratory data analysis.
Part 2: The Tools of Data Science
Now that we’ve answered the question “what is data science?”, let’s talk about the tools that data scientists use to do their work.
As we mentioned before, one of the most important tools in a data scientist’s toolkit is exploratory data analysis (EDA).
EDA is a approach to analyzing data that focuses on understanding the relationships between variables and identifying patterns.
EDA generally starts with visualizing the data using graphs and plots so that any patterns or relationships are immediately apparent.
Once any initial patterns have been identified, further analysis can be done using statistical methods like hypothesis testing to confirm or refute these patterns.
Finally, once all the findings have been gathered together, they can be communicated using tables, figures, and narratives.
Another important tool for data scientists is statistical analysis.
Statistical methods are used to test hypotheses about relationships between variables and to make predictions based on these relationships. Common statistical methods include regression analysis, ANOVA, and chi-squared tests.
Finally, machine learning algorithms are increasingly being used by data scientists to automatically find patterns in data sets too large or complex for humans to discern.
There are many different types of machine learning algorithms; some common ones include classification algorithms, clustering algorithms, and artificial neural networks.
Part 3: Getting Started in Data Science
So now that you know what data science is and what tools are used by practitioners in the field, you might be wondering how you can get started if you’re interested in pursuing a career in data science.
There are many ways to break into the field; some people get started with a degree in computer science or mathematics, while others come from more traditional backgrounds like anthropology or history.
No matter your background, there are three things you’ll need to learn in order to be successful as adata scientist: programming languages (like R or Python), statistical methods (like regression or hypothesis testing), and exploratory data analysis (like plotting or tablemaking). Luckily, there are many resources available online—like Datacamp!—to help you learn these skills.
Data Science Course – Why You Should Consider It
It is always good to learn new skills, understand its key concepts and get a deep learning and further information. A data science course can benefit anyone looking to transition into a career in data science or sharpen their skills.
Although the field of data science is male-dominated, women+ experience many unique advantages that make them top performers in the field. .
We’ll discuss some of the reasons why a data science course may be the right choice for you but first lets take a look at some of the typical steps involved in a data science project:
1. Defining the problem
2. Wrangling and exploring the data
3. Building models
4. Evaluating model performance
5. Deploying the model (optional) 5.1 Retraining the model (optional)
6. Communicating results
Data scientists use a variety of tools to perform each stage of a data science project, but some of the most popular tools are Python, R, SQL, and Tableau.
Jupyter Notebooks are an important tool for sharing your work with others. A Jupyter Notebook is an interactive computational environment that contains code cells and Markdown cells.
Code cells allow you to write and run code, while Markdown cells allow you to write explanatory text. You can share your Jupyter Notebooks online so that others can view them without having to install any software themselves.
If you are planning to take online courses and learn individual modules through interactive experience with expert instruction to fast track your career for this high demand job ready new skill to start your new career, then there are countless career opportunities out there that do not have any required level or initial assessment and can lifetime regular access to learn at your own pace.
Flexibility is a big reason; particularly if you’re already working full-time, the ability to pursue your data science education on your own time instead of having to take time off from your job is a huge advantage.
The popularity of data science courses on campus are also increasing the appeal of online courses. Many students who want to take these courses on campus find them overenrolled, or else so crowded that lectures are challenging to follow and access to faculty is lacking.
Types of Data
There are three main types of data: categorical (nominal), ordinal, and numerical (continuous).
Categorical data can be further divided into binary (two categories) and multiclass (more than two categories).
Categorical Data:
Examples:
- Gender (male/female)
- Genre (comedy/drama/action)
- Type of pet (cat/dog/bird)
Ordinal Data:
Examples
- Movie ratings (G/PG/PG-13/R)
- Grade levels (freshman/sophomore/junior/senior)
- Satisfaction ratings (low/medium/high)
Numerical Data:
Examples
- Age
- Height
- Salary
Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a process for summarized understanding dataset through visualization techniques such as plotting histograms, box plots, and scatter plots; calculating summary statistics; and identifying outliers or unusual observations.
By doing EDA we can develop hypotheses about potential features that could be useful for building predictive models later on in the process.
EDA was performed on Kaggle’s Titanic dataset using Python’s Pandas and Matplotlib libraries Modeling. After performing EDA on our dataset(s), we can start building predictive models using machine learning algorithms.
In this example, we built a logistic regression model to predict whether or not a passenger on the Titanic would survive given information such as their age, gender, class, etc.
The model had an accuracy score of 79% which means that it correctly predicted whether or not passengers would survive 79% of the time.
In fact, our data shows that more than 1.7 million job postings asked for data science skills in 2018.” Learning this future-proof skill set can help you enter the next stage of your career, whether that’s advancing in your current profession or exploring an exciting and lucrative field.
Data science is amazing cool awesome! And this one time I totally used machine learning to …. do something something predictive modeling yay go me!
A background in computer science is not required . . .
One common misconception about data science is that you need a computer science degree to get started. This couldn’t be further from the truth!
While coding knowledge is helpful, it is not necessary to begin a career in data science. There are many online resources and bootcamps that can teach you the coding skills you need to get started.
And once you’re working as a data scientist, you’ll have plenty of opportunities to learn on the job.
You don’t need to be good at math . . .
Another myth about data science is that you need to be good at math to be successful.
Again, this simply isn’t true!
While a strong foundation in math is helpful, there are many aspects of data science that don’t require advanced mathematical skills.
For instance, data cleaning and exploratory data analysis can be performed without any complex mathematical calculations. And if you’re interested in machine learning, there are many software libraries that will do the heavy lifting for you.
So if you’re not confident in your math abilities, don’t let that stop you from pursuing a career in data science!
There are many opportunities for women+ in data science . . .
Despite being male-dominated, the field of data science offers many opportunities for women+.
In fact, according to Forbes, women actually hold an advantage over men when it comes to certain skillsets required for success in data science.
These skillsets include empathy, social intelligence, and writing ability. Empathy is important for understanding customer needs and coming up with creative solutions. Social intelligence helps with team collaboration and networking.
Computer science is one of the most common subjects that online learners study, and data science is no exception.
While some learners may wish to study data science through a traditional on-campus degree program or an intensive “bootcamp” class or school, the cost of these options can add up quickly once tuition as well as the cost of books and transportation and sometimes even lodging are included.
And writing ability is paramount for creating clear and concise reports detailing your findings.
So if you’re a woman+ interested in data science, know that you have plenty to offer!
Conclusion:
Learning data science can feel daunting, especially if you’re starting from scratch. But our introduction to data science course is perfect for women who want to learn more about this growing field.
Over the span of 12 weeks, you’ll cover everything from basic statistics to machine learning and big data. And we’re committed to making this course accessible to as many women as possible.
So, what are you waiting for?