| Description |
1 online resource (404 p.) |
| Contents |
Cover -- Copyright -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Breaking into the Data Science Field -- Chapter 1: Exploring Today's Modern Data Science Landscape -- What is data science? -- Exploring the data science process -- Data collection -- Data exploration -- Data modeling -- Model evaluation -- Model deployment and monitoring -- Dissecting the flavors of data science -- Data engineer -- Dashboarding and visual specialist -- ML specialist -- Domain expert -- Reviewing career paths in data science -- The traditionalist -- Domain expert |
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Off-the-beaten path-er -- Tackling the experience bottleneck -- Academic experience -- Work experience -- Understanding expected skills and competencies -- Hard (technical) skills -- Soft (communication) skills -- Exploring the evolution of data science -- New models -- New environments -- New computing -- New applications -- Summary -- References -- Chapter 2: Finding a Job in Data Science -- Searching for your first data science job -- Preparing for the road ahead -- Finding job boards -- Beginning to build a standout portfolio -- Applying for jobs -- Constructing the Golden Resume |
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The perfect resume myth -- Understanding automated resume screening -- Crafting an effective resume -- Formatting and organization -- Using the correct terminology -- Prepping for landing the interview -- Moore's Law -- Research, research, research -- Branding -- References -- Part 2: Manipulating and Managing Data -- Chapter 3: Programming with Python -- Using variables, data types, and data structures -- Indexing in Python -- Using string operations -- Initializing a string -- String indexing -- Using Python control statements, loops, and list comprehensions |
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Conditional statements such as if, elif, and else -- Loop statements such as for and while -- List comprehension -- Using user-defined functions -- Breaking down the user-defined function syntax -- Doing "stuff" with user-defined functions -- Getting familiar with lambda functions -- Creating good functions -- Handling files in Python -- Opening files with pandas -- Wrangling data with pandas -- Handling missing data -- Selecting data -- Sorting data -- Merging data -- Aggregation with groupby() -- Summary -- References -- Chapter 4: Visualizing Data and Data Storytelling |
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Understanding data visualization -- Bar charts -- Line charts -- Scatter plots -- Histograms -- Density plots -- Quantile-quantile plots (Q-Q plots) -- Box plots -- Pie charts -- Surveying tools of the trade -- Power BI -- Tableau -- Shiny -- ggplot2 (R) -- Matplotlib (Python) -- Seaborn (Python) -- Developing dashboards, reports, and KPIs -- Developing charts and graphs -- Bar chart -- Matplotlib -- Bar chart -- Seaborn -- Scatter plot -- Matplotlib -- Scatter plot -- Seaborn -- Histogram plot -- Matplotlib -- Histogram plot -- Seaborn -- Applying scenario-based storytelling -- Summary |
| Summary |
Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book Description The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you'll find tips on job hunting, resume writing, and creating a top-notch portfolio. You'll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you'll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job. What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews |
| Notes |
Description based upon print version of record |
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Chapter 5: Querying Databases with SQL |
| Subject |
Employment interviewing.
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Information science -- Vocational guidance
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Electronic data processing -- Vocational guidance.
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| Form |
Electronic book
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| Author |
Stubberfield, Aaren
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Baltes, Angela
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| ISBN |
9781805120193 |
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1805120190 |
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