{"id":23623,"date":"2023-06-27T15:33:00","date_gmt":"2023-06-27T22:33:00","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=23623"},"modified":"2023-06-28T00:25:53","modified_gmt":"2023-06-28T07:25:53","slug":"ridge-regression-python","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/ridge-regression-python\/","title":{"rendered":"Ridge Regression with SGD Using Python: Hands-on Session with Springboard\u2019s Data Science Mentor"},"content":{"rendered":"\n

In the field of machine learning, regression analysis is one of the most common and popular techniques to understand relationships between variables. Linear regressions are typically used in these scenarios. However, when you see high multicollinearity in your linear regression \u2014 which is common when there are a large number of parameters \u2014 you need a way to optimise bias before you can build models. It is here that ridge regression with SGD comes handy.<\/p>\n\n\n\n

Ridge Regression with Stochastic Gradient Descent Using Python<\/h2>\n\n\n\n

Let’s first understand ridge regression and stochastic gradient descent algorithm individually.<\/p>\n\n\n\n

What is Ridge Regression?<\/h2>\n\n\n\n

Ridge Regression is a commonly used method of processing regression data with multicollinearity. When independent variables in a multiple regression model are correlated, we call it multicollinearity. This might cause coefficient estimates to change erratically, as you make changes to the independent variables in your model. Ridge regression reduces standard errors by adding a degree of bias to the regression estimates.<\/p>\n\n\n\n

What is Stochastic Gradient Descent?<\/h2>\n\n\n\n

In plain English, gradient descent means sliding down a slope. In statistics, this might be a descent down the slope of the error surface to reach its lowest point. However, it doesn\u2019t fall straight down the slope. A gradient descent algorithm follows an iterative process that travels down the slope in steps, using local information, until reaching the lowest point.<\/p>\n\n\n\n

Stochastic means random. Stochastic gradient descent introduces randomness by selecting data points at random during each iteration. What this does is help significantly reduce the burden of computation in the process of minimising the sum of squared residuals.<\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Leoman<\/a>

Leoman Momoh<\/p>

Senior Data Engineer at Enterprise Products<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Jonathan<\/a>

Jonathan King<\/p>

Sr. Healthcare Analyst at IBM<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Sam<\/a>

Sam Fisher<\/p>

Data Science Engineer at Stratyfy<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

Reducing Loss with Gradient Descent<\/h2>\n\n\n\n

You can calculate the gradient using basic calculus \u2014 differentiation \u2014 or use existing functions in Python. Then, you move to the next point, calculate the gradient and so on. The jump you make until the next point is the learning rate. It is important to choose the right learning rate.<\/p>\n\n\n\n

Before fitting a model or building an algorithm<\/a>, divide the dataset into three sets \u2014 training, validation, and test sets. Then, you must normalize the features. Post this, you can calculate the loss. Do a gradient check. And use gradient descent algorithms to minimise the loss.<\/p>\n\n\n\n

Preparing to Perform Ridge Regression<\/h2>\n\n\n\n

Before we get to how to use ridge regression, there is one thing that\u2019ll be very helpful to know: How to remove for-loop from code and make the code much faster using vectorization and broadcasting. We primarily use two libraries \u2014 Pandas and Numpy. Pandas is for relational databases, time series data, etc.; NumPy for mathematical computation; we can also use PyPlot for plotting.<\/p>\n\n\n\n

Let\u2019s take an example of matrix multiplication.<\/p>\n\n\n\n

\"ridge<\/figure>\n\n\n\n

While performing multiplication using for-loop for this small matrix, we notice that the computation takes about 100 milliseconds. However, in the real world, your matrices won\u2019t be this small. You will be working with much larger data, which will take significant time and resources for computation. First work to reduce that. You can do this with element-wise operations, which reduced the same computation to under 30ms. Or use broadcasting, which also works for 2D arrays \u2014 this can expedite computation, while keeping the code simple and elegant.<\/p>\n\n\n\n

To learn how to do each of these steps, watch Springboard\u2019s data science mentor Neeraj Agarwal demonstrate with examples in this Youtube tutorial. He also shows how each of the steps is impacting the models and teaches how to improve them. You can get the data and notebook he\u2019s using by leaving your details in this form<\/a>.<\/p>\n\n\n\n

For further reading, check out the job description for data scientist here<\/a> and learn more about data science<\/a>.<\/em><\/p>\n\n\n\n

Companies are no longer just collecting data. They\u2019re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists \u2013 the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.<\/p>\n\n\n\n

In this article, we\u2019ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you\u2019re more drawn out to the creative side or interested in the strategy planning part of data architecture, there\u2019s a niche for you. <\/p>\n\n\n\n

Is Data Science A Good Career?<\/h2>\n\n\n\n

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It\u2019s an interdisciplinary field that keeps the work varied and interesting.<\/p>\n\n\n\n

10 Data Science Careers To Consider<\/h2>\n\n\n\n

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.<\/p>\n\n\n\n

<\/div><\/div><\/div><\/div>\n\n\n\n

<\/p>\n\n\n\n

Data Scientist<\/h3>\n\n\n\n

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research \u2013 all depending on the field they operate in.<\/p>\n\n\n\n

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. <\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

A data scientist\u2019s career starts with a solid mathematical foundation, whether it\u2019s interpreting the results of an A\/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. <\/p>\n\n\n\n

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses<\/a> and certifications that demonstrate their expertise and willingness to learn.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

The average salary of a data scientist in the US is $156,363<\/a> per year.<\/p>\n\n\n\n

Data Analyst<\/h3>\n\n\n\n

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.<\/p>\n\n\n\n

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps<\/a> to acquire these skills. <\/p>\n\n\n\n

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

The average base salary of a data analyst is $76,892<\/a> per year.<\/p>\n\n\n\n

Business Analyst<\/h3>\n\n\n\n

Business analysts often have an essential role in an organization, driving change and improvement. That\u2019s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. <\/p>\n\n\n\n

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master\u2019s degree, especially in more complex industries or corporate environments.<\/p>\n\n\n\n

Employers also value a business analyst\u2019s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

A business analyst can earn an average of $84,435<\/a> per year.<\/p>\n\n\n\n

Database Administrator<\/h3>\n\n\n\n

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. <\/p>\n\n\n\n

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it\u2019s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. <\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Database administrators earn an average salary of $77,391<\/a> annually.<\/p>\n\n\n\n

Data Engineer<\/h3>\n\n\n\n

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. <\/p>\n\n\n\n

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Data engineers earn a whooping average salary of $125,180<\/a> per year.<\/p>\n\n\n\n

Database Architect<\/h3>\n\n\n\n

A database architect\u2019s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. <\/p>\n\n\n\n

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

If you\u2019re aiming to excel as a database architect but don\u2019t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don\u2019t forget programming languages – SQL, Python, or Java. <\/p>\n\n\n\n

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard<\/a>.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Data architecture is a very lucrative career. A database architect can earn an average of $165,383<\/a> per year.<\/p>\n\n\n\n

Machine Learning Engineer<\/h3>\n\n\n\n

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. <\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K<\/a> per year, one of the highest-paying AI careers<\/a>.<\/p>\n\n\n\n

Quantitative Analyst<\/h3>\n\n\n\n

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. <\/p>\n\n\n\n

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. <\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

A quantitative analyst earns an average of $173,307<\/a> per year.<\/p>\n\n\n\n

Data Mining Specialist<\/h3>\n\n\n\n

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. <\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Data mining specialists earn $109,023<\/a> per year.<\/p>\n\n\n\n

Data Visualisation Engineer<\/h3>\n\n\n\n

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data\u2019s context. <\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

The average annual pay of a data visualization engineer is $103,031<\/a>.<\/p>\n\n\n\n

Resources To Find Data Science Jobs<\/h2>\n\n\n\n

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.<\/p>\n\n\n\n

Job Boards<\/h3>\n\n\n\n

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. <\/p>\n\n\n\n

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.<\/p>\n\n\n\n

Online Communities<\/h3>\n\n\n\n

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.<\/p>\n\n\n\n

Network And LinkedIn<\/h3>\n\n\n\n

Don\u2019t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the \u201cEasy Apply\u201d options, as you\u2019ll be competing with thousands of applicants who bring nothing unique to the table.<\/p>\n\n\n\n

FAQs about Data Science Careers<\/h2>\n\n\n\n

We answer your most frequently asked questions.<\/p>\n\n\n\n

Do I Need A Degree For Data Science?<\/h3>\n\n\n\n

A degree is not a set-in-stone requirement to become a data scientist<\/a>. It\u2019s true many data scientists hold a BA\u2019s or MA\u2019s degree, but these just provide foundational knowledge. It\u2019s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.<\/p>\n\n\n\n

Does Data Science Need Coding?<\/h3>\n\n\n\n

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.<\/p>\n\n\n\n

Is Data Science A Lot Of Math?<\/h3>\n\n\n\n

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.<\/p>\n\n\n\n

What Skills Do You Need To Land an Entry-Level Data Science Position?<\/h3>\n\n\n\n

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you\u2019re acing problem-solving, critical thinking, and effective communication.<\/p>\n\n\n\n

Since you\u2019re here\u2026<\/strong>Are you interested in this career track? Investigate with our free guide to what a data professional actually<\/em> does<\/a>. When you\u2019re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp<\/a> which will help you land a job or your tuition back!<\/p>\n","protected":false},"excerpt":{"rendered":"

In the field of machine learning, regression analysis is one of the most common and popular techniques to understand relationships between variables. Linear regressions are typically used in these scenarios. However, when you see high multicollinearity in your linear regression \u2014 which is common when there are a large number of parameters \u2014 you need […]<\/p>\n","protected":false},"author":100,"featured_media":46653,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[],"class_list":{"0":"post-23623","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/23623"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=23623"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/23623\/revisions"}],"predecessor-version":[{"id":46654,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/23623\/revisions\/46654"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/46653"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=23623"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=23623"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=23623"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=23623"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}