Kids in Kindergarten

I gathered some stats that will help us define what is “normal” in terms of illnesses in child care. What is considered to be normal? According to Mayo Clinic, an average of 7 to 8 infections a year for every kid and more (up to 12 colds a year) for children who are in child care or when they start school. It’s also typical for kids to have symptoms lasting up to 14 days. And sometimes a cough can last up to 6 weeks. That means kids can be sick for a majority of the year and still have a pretty typical immune system. I found other stats on recovery that mention a 7 to 10 days (and even earlier) recovery time, although a cough can last up to 3 weeks. When can my child return to daycare? Most centres have a 24 hour policy. If the child is symptom free for 24 hours, or has been on medication for 24 hours, and they feel better, they can return. Some centres increase this time period to 48 hours during an outbreak in order to prevent further spread of an illness. (according to this website) Another recommendation: Often, a child is not allowed to return to the centre until they’ve been fever-free (or diarrhea-free) for 24 hours. (according to this website) Department of Health and Environmental Control (DHEC) website advise: Most children with mild colds who have no fever and who feel well enough to go to school or childcare do not need to stay home. Most colds spread in the 1-3 days before children show symptoms such as a runny nose or minor cough. (according to this brochure) Useful cold and flu stuff to have at home for babies and toddlers (Note: I participate in the affiliate amazon program. This post may contain affiliate links from Amazon or other publishers I trust (at no extra cost to you). I may receive a small commission when you buy using my links, this helps to keep the blog alive! See disclosure for details.) Electric nasal aspirator A working thermometer, we have have both a non-contact forehead one and a flexible digital thermometer Saline spray Topical vapor rub Age appropriate fever-reducing medications When to start worrying? Jeffrey Modell Foundation’s ( that educates on Primary immunodeficiency: PI) lists out these 10 warning signs that might direct you to a  physician). PI causes children and adults to have infections that come back frequently or are unusually hard to cure.  4 or more new ear infections in one year  2 or more serious sinus infections in one year  2 or more months on antibiotics with little effect  2 or more cases of pneumonia within one year  Failure of an infant to gain weight or grow normally ( check official WHO Growth Standards here)  Recurrent, deep skin or organ abscesses  Recurrent thrush in the mouth or elsewhere on the skin after age one  Need for intravenous antibiotics to clear infections  2 or more deep-seated infections  A family history of primary immunodeficiency   The bright side A study (“Daycares have a reputation for being germ factories,” lead author Sylvana Cote of the University of Montreal, in Quebec)  found that toddlers in group child care get sick more often than toddlers who stay at home, but found those same kids get sick less often than their peers during the primary school years.   A cool visualisation related to our immune system: Pic Source: https://twitter.com/rajivshivan/status/1233692472934531073   This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!

autoEDA Python

Run automatic EDA (Exploratory Data Analysis) in Python With 2 lines of Python code you’ll get a HTML report with all the important EDA aspects you need to understand your raw data. Install pandas_profiling: pip install pandas_profiling Import pandas_profiling: from pandas_profiling import ProfileReport Create the autoEDA report: profile = ProfileReport(rawdataTbl, title=”Profiling Report”) profile.to_file(“Profiling Report.html”) Check this website if you need additional configuration for your report: https://pypi.org/project/pandas-profiling/     This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!

superheroes pj masks

2 studies, 2 views on the impact of superheroes over kids   There are 2 primary studies that looked into the development impact of superheroes on kids: The 1st study suggest that parents may need to help their preschool children filter the messages arising from superhero media in order to minimize aggressive behavior outcomes and promote defending gestures on behalf of their most vulnerable peers. The batman effect study proves that positive effect is an active cognitive strategy that moves us forward with confidence as we stay in the stressful situation but with our alter-ego leading the charge: adopting an alter ego can also help children concentrate on a complex card game, in which they had to follow complex rules that kept on changing.   Sailor Moon was my superhero obsession when I was a kid. Back in the day, all my friends had a favourite superhero to impersonate when we were playing together mimicking the cartoon. It was non-violent fun and games. I don’t recall any bad scenes while playing and mimicking Sailor Moon (maybe that more girls wanted to be “the Sailor Moon” at the same time”). via GIPHY Fast forward to 2022, Bianca, my 3 years old toddler, loves PJ Masks. They are 3 superheroes for the little ones: by day, they go to school like all the other kids their age. By night, this brave band of heroes dons their magic pyjamas, and sets out to face fiendish villains to stop them messing with your day! I read in this study that “Kids pick up on the aggressive themes and not the defending ones.” and I panicked a bit. 5 min later I realised that: I was Sailor Moon and I “punished” evil forces in the name of the moon. So, maybe the truth lies somewhere in between. As I am a true believer that “All models are wrong, but some are useful“, I look deeper into the matter: The Batman effect study investigated the benefits of self-distancing on young children’s perseverance: 180 four- and six-year-old children were asked to complete a repetitive task for 10 minutes while having the option to take breaks by playing an extremely attractive video game. Six-year-olds persevered longer than four-year-olds. Nonetheless, across both ages, children who impersonated an exemplar other – in this case, a character, such as Batman—spent the most time working, followed by children who took a third-person perspective on the self, or finally, a first-person perspective. Alternative explanations, implications, and future research directions are discussed. Results [wpdatachart id=1] The results of the study were analyzed by applying Analysis Of Variance ( ANOVA ) on the percentage of time on a task and comparing the 3 groups: self-immersed, 3rd person, and exemplar. ANOVA is normally used to analyze experimental studies. Analysis of Variance is a generalization of the hypothesis test for equality of means. Here, you have multiple populations, and you want to see if any of the population means are different from the other means. That means that the null hypothesis is that ALL the population means are equal to each other. The alternative hypothesis is that at least two of the means are not equal. Across both ages, children who impersonated an exemplar other – in this case a character, such as Batman—spent the most time working, followed by children who took a third-person perspective on the self, or finally, a first-person perspective. Interesting, right? Another article investigated the matter and concluded that some studies measure with low facts that boys are more impacted , others that there might be positive effects.   (Note: I participate in the affiliate amazon program. This post may contain affiliate links from Amazon or other publishers I trust (at no extra cost to you). I may receive a small commission when you buy using my links, this helps to keep the blog alive! See disclosure for details.) Do you want to understand the basic concepts of statistics? Get this book: If you want to design and analyze your own experiments, then get this one: My view on this I like superheroes and I like to think that they can be 100% beneficial for kids… On the other hand, I saw plenty of almost-violent toddlers at the playground and none were wearing a cape. The parents should stop the aggressiveness. What I personally take away from this study, is that a toddler needs more clarification about what a superhero is.  Superhero stories are often complex, but preschoolers may not fully understand the complexities behind the violence or aggression they witness. Superheroes represent the good, so it’s handy to explain the opposite of bad :). Exposure to superheroes ( and screen time)  should be allowed in moderation. We should not be afraid of one single study. Kids’ personalities will give the general trend: if a toddler expressed aggressiveness, then yes, maybe exposure to superheroes is something negative for that kid. If your child’s aggressive acts are frequent and severe, or your efforts to curb them have no effect, you’ll need to consult your pediatrician or a trained mental health professional, such as a child psychologist or psychiatrist.   My summary on this is that we all need a superhero in our lives…   View this post on Instagram   A post shared by Sailor Moon Memes (@sailormoon.memes)   If you suffer from Sailor Moon nostalgia, you can get something on Amazon. Bianca has the 3 PJ Masks costumes, the watch, and another amulet. She loves them all…   This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!

9 month old baby

It’s amazing how your 9 months old babbling baby starts to show joy and interest in all the small things and also gets annoyed when something is forbidden or out of reach.  In this blog post, you will find the important milestones for a 9-month baby – the normal growth range, sleep, feeding, activities, must-have toys, and a daily schedule. Milestones When researching my content, I use official websites such as the CDC ( Centers for Disease Control and Prevention). They list the below as the milestones a child should reach by the end of 9 months: Social/Emotional Milestones Is shy, clingy, or fearful around strangers Shows several facial expressions, like happy, sad, angry, and surprised Looks when you call her name Reacts when you leave (looks after, reaches for you, or cries) Smiles or laughs when you play peek-a-boo Language/Communication Milestones Makes a lot of different sounds like “mamamama” and “bababababa” Raises arms to be picked up Cognitive Milestones (learning, thinking, problem-solving) Looks for objects when dropped out of sight (like his spoon or toy) Bangs two things together Movement/Physical Development Milestones Manages to get to a sitting position on their own Moves things from one hand to another Uses fingers to “rake” food towards himself Sits without support By 9 months of age some babies might start using the furniture to try to stand and even climb stairs on all four.   Baby Growth    The normal growth range at 9 months old differs between boys and girls. Length Girls range from 63 – 78 cm (25-31 in.)  in height; Boys range from 65 – 79 cm (26-31 in.)  in height; Weight Girls range from 6200-12100 gr (14-27 lb.) in weight; Boys range from 6900-12400 gr (15-27 lb.) in weight.   Another important aspect is that the growth rate of the baby will slow down after six months: you will expect the baby to gain about 500 gr (1 lb.) and 1 cm (3/8 inch) per month moving forward.  Expect your baby to triple his or her birth weight by about age 1 year. For more info on this, check the Growth WHO Standards table and other baby standards here.   Baby Sleep   At this age your baby will have 2 naps a day with the below structure: 1st nap at 9 am; 2nd nap at 1 pm; 6:30 – 7 pm bedtime (with no feedings during the night). It is normal for your baby to get up between 6 and 7 am in the morning, but also normal at this age to wake up between 5 to 6 am. Your baby might seem well rested ( probably slept for 11 hours during the night), but 12 hours is also achievable. There are a few things to try in order to push the wake up hour to 7 am: keep a sleep log to note down what you change in the routine and how it impacts your baby’s sleep. Get a free here; if you don’t have 80% darkness in the room, you should try harder to achieve it. I wrote here about the important room and baby sleep training prep; if your baby sleeps less than 1.5hr during the day, move bedtime 15 mins earlier each day until the baby will get on track (remember: sleep log!); if your baby sleeps more than 3hr during the day, try and cap the longest nap (so the baby will be prepared for the 12 hours night-sleep). if you tried all of these, just let the baby enjoy some crib time (don’t make light in the room until it gets as close as possible to 6:30-7 am).   Baby Feeding   This is the perfect time for the baby to enjoy 3 solid meals a day. See some of the solids that can be introduced below: Try waiting at least 2 days after introducing new food to the baby to check for any allergic reactions. If you notice symptoms such as diarrhea and vomiting it might mean that the baby is allergic to a certain food type. Don’t forget to start giving the baby water. If you use bottled water, check the level of sodium or sulphate. Look on the label to check the levels: Sodium (Na) <= 200 mg/l ; Sulphate (‘SO’ or ‘SO4’) <= 250 mg/l.   Baby Eyes   Your baby’s eyesight is still maturing, but now he/she is able to see pretty well near and far and focus on moving objects. At this age, some babies will have the patience to “read” books, but most of the babies will just eat them.   Activities   Sky is the limit, there are so many activities that you can do without the need of any special toys or materials: practice crawling and standing up practice getting down from the bed kick a ball crawl up a step play the drums and the piano drop objects in a container get to a hidden toy clapping and playing peekaboo calling “Mama” or “Dada” giving you toys experiencing cold and hot meet animals saying bye bye drinking from a cup learning to self feed learning the meaning of Yes and No   Must-have toys   (Note: I participate in the affiliate amazon program. This post may contain affiliate links from Amazon or other publishers I trust (at no extra cost to you). I may receive a small commission when you buy using my links, this helps to keep the blog alive! See disclosure for details.) When you exhaust all the activities that use no materials, get some of the below toys (9M +); your baby will love them: Sorting Bin. It allows babies to slip the blocks through elastic bands at any position. Get them here. Musical Press and Go Inchworm Toy. This toy will attract your little ones to chase and crawl, which will build muscle strength and gross motor skills as they play; buy one here. Interactive 3D Fabric Activity Book. Stimulate Growth of Vision and Brain: Colorful content and

Mentoring for Data Science

Coaching and Mentoring are very important to me as they guided me throughout my career in Data and more recently, the career of a Data Scientist. They are extremely relatable, but there are still some key differences between the two.  In this article I’ll explain the difference between the two and how you can benefit from having a coach or/and a mentor.    What is mentoring?   Wiki says: “Mentorship is the influence, guidance, or direction given by a mentor. A mentor is someone who teaches or gives help and advice to a less experienced and often younger person. In an organisational setting, a mentor influences the personal and professional growth of a mentee. Most traditional mentorships involve having senior employees mentor more junior employees, but mentors do not necessarily have to be more senior than the people they mentor. What matters is that mentors have experience that others can learn from.”   How can mentoring help you?   Mentoring is like a student-teacher relationship. In Data Science, a mentor can be a Senior/Consultant Data Scientist who will answer your questions and raise questions you hadn’t considered to help you meet a certain objective / skill / master a specific tool.   I had mentors while working for Symantec and DELL as these companies had formal mentoring programs running. The “issue” was that they were not Analytics / Data Science specific, and, at that time I needed  domain specific support.  To fill this need, I went on LinkedIn (as they try to promote professionals as mentors), but people I contacted  did not reply or they were very expeditive. I looked deeper and I found these guys: MentorCruise. They are a dedicated website for mentoring services. I found them cool and I registered as a Mentor myself. You can find my profile here: Having a mentor and being one will give you confidence and great satisfaction that you can help other professionals similar to you. At the moment I’m mentoring two data professionals. I really like the interaction and the feedback I got so far . Click here if you want to become a mentor on MentorCruise. While working for Symantec, I attended a 3 days course on coaching and I got to experience being both a coach and a coachee. Nowadays, there are more in-depth specialisation and coaching is a profession. I wanted you to learn about coaching from the best, so I asked a former colleague of mine who is a trained coach to guest post for www.thebabydatascientist.com .    Claudia is a Positive Psychologist and Coach specialising in career and leadership coaching for people in tech. She is based in Ireland and works globally with people who want to be happier at work.   Let’s see what Claudia advises: What is coaching?   Coaching can be defined in many ways. In essence, coaching is a space for personal development through a thought-provoking and creative process that inspires you to maximise your personal and professional potential (International Coaching Federation).   In the business world, workplace coaching is often used as an effective tool to optimise performance and unlocking untapped potential. Often, the goal of coaching interventions in the workplace is to build team cohesion, employee productivity and motivation, and develop authentic leadership skills.   However, coaching is much more than just a tool to increase your productivity. Coaching is a journey to more self-awareness, a space to help you learn more about yourself. Typically you will gain a greater understanding of values and your strengths and learn how they impact your behaviour, your thought processes and your emotions.  There is great power in understanding that triangular relationship – it’s the starting point to self-determined behavioural change.   How is coaching different from mentoring?   Coaching is built on the core belief that you, the coach or the client, already have all the answers, skills and knowledge within yourself to reach your goals. The coach’s role then is to help you uncover the answers you are seeking through a non-directive and non-judgmental dialogue.    That means coaching is less directive than mentoring. It is not about giving advice, it is about empowering you to discover what is best for you – in work and in life. While a mentor shares their own experiences and industry knowledge with the mentee, a coach is not necessarily an expert in your line of work. A coach asks you thought-provoking questions, offers new perspectives and guides you in the process to come to a new insight that empowers you to create a path forward that is right for you.   How can coaching help you?   Coaching is a very versatile approach to personal development and can help you achieve goals, make difficult decisions or face challenges with more confidence. Below are a few examples of typical coaching topics for career coaching.   How coaching can help you in your career   Qualified and accredited coaches often specialise on a topic, or career stage a client is in. Here are some examples of what career coaching can help you with:   Career coaching. This can be anything from finding career clarity, building your confidence to interview effectively, supporting you in managing up, progressing in your career or building your own brand.    Leadership coaching. Leadership coaches work with new and established leaders and often are an independent sounding board for their clients. Typical topics in leadership coaching include finding your authentic leadership style, building confidence as a leader, communicating assertively, employee motivation, and building high performing teams.   Career and leadership coaching is ideal for people who suffer from imposter feelings, have trouble creating or maintaining a healthy work-life balance or find it difficult to be themselves at work.   Your coaching readiness checklist   Are you ready to find out if you are ready to engage a coach? Here is a handy checklist to determine your coaching readiness:   I am determined to make a change I am ready to ask myself

Data Science lifecycle and steps

The lifecycles below will guide you from the initial phase of a Data Science project through the project’s successful completion. It will enable you to divide the work within the team, estimate efforts, document all the steps of your project, and set realistic expectations for the project stakeholders. I believe that implementing a standard process model should be the Data Science norm, not the exception.   CRISP-DM for Data Science I’ve been using CRISP-DM (Cross Industry Standard Practice for Data Mining) as a process model for my Data Science project execution work for a few years and I can confirm that it works. The process consists of 6 major steps and all the Data Science sub-tasks can be mapped as below:     Data Science Lifecycle   While studying for the DELL EMC Data Science Associate Exam, I learned that DELL also recommends a Data Science lifecycle. In the course, the Data Science lifecycle is also divided into 6 phases, named differently, but having the same functions: Discovery – Data Prep – Model Planning – Model Building – Communicate Results – Operationalize. The Data Science lifecycle it’s an iterative process; you’ll move through the phases if sufficient information is available. 1. Business Understanding Determine business objectives and goals Assess situation Produce project plan   It might seem like you need to do a lot of documentation even from the initial phase (and this is considered one of the few weaknesses of CRISP-DM), but a formal one-pager with the signed-off Business Case, that clearly states both business and machine learning objectives, along with listing the past information related to similar efforts should be documented. This exercise will prioritize items in your backlog and protect you from scope creep.   Pic. Source: The Machine Learning Project Checklist   2. Data Understanding Collect initial data Describe data Explore data Verify data quality   This is the “scary”, time-consuming and crucial phase. I would sum it up as ETL/ELT + EDA = ♥ Acquiring data can be complex when it originates from both internal and external sources, in a structured and unstructured format, and without the help of a Data Engineer (they are so Rare Unicorns nowadays). Without having good quality data, Machine Learning projects will became useless. This part is missing in most online Data Science courses/competitions, so Data Scientists should learn how to do ETL/ELT by themselves. EDA (Explanatory Data Analysis) got simple as this can be automatically performed with packages like pandas profiling, sweet viz, Dtale, autoviz in Python or DataExplorer, GGally, Smarteda, and tableone in R. Some teams will use a mix of automated and custom EDA features.   Data Preparation Select data Clean data Construct data Integrate data Format data   This is another code-intensive phase and it means knowing your dataset in great detail so it gives you the confidence to select data for modeling, clean it and perform feature engineering. The EDA stage will tell you if you have missing data, outliers, highly correlated features, and so on, but in the Data Prep stage, you have to decide what imputation strategy to apply. You might go back to Business Understanding in case you form a new hypothesis or you require confirmations from Subject Matter Experts.  Featuring engineering is a hot topic here. Until recently, this was a tedious task, mostly manual that involved creating new variables based on available features using data wrangling techniques. Data Scientists will use pandas in python and dplyr in R. Nowadays, frameworks like Featuretools will save the day. I wouldn’t rely 100% on an automated feature engineering tool, but overall, it’s a nice addition to a Data Science project. Feature selection Feature importance: some algorithms like random forests or XGBoost allow you to determine which features were the most “important” in predicting the target variable’s value. By quickly creating one of these models and conducting feature importance, you’ll get an understanding of which variables are more useful than others. Dimensionality reduction: One of the most common dimensionality reduction techniques, Principal Component Analysis (PCA) takes a large number of features and uses linear algebra to reduce them to fewer features. By the end of this phase, you’ve 70% – 80% completed the project. But the fun begins in the next phase:   Modeling Select modeling techniques Generate test design Build model Assess model   Once it is clear which algorithm to try (or try first), you’ll have to: from sklearn.model_selection import train_test_split . Splitting your dataset into train and test it is key for building a performant model. You’ll build the model on the trained dataset (usually consisting of 70% of your data) and check how it performed on the test dataset (30% of data). Don’t forget to: random_state / seed  This code will help you reproduce the same random split result. Based on the complexity of the model, building the code can be as quick as writing 2 lines of code. For Python scikit-learn is your model building library. In R you have packages like caret, e1071, xgboost, randomForest, etc. While assessing the accuracy of your model, you might decide to go back to the previous step/s and reiterate. Time spent on modeling is subjective as models can be improved with more tuning, but if time is more valuable than the % increase in accuracy, you’ll want to move to the next step. Some rule of thumb in modeling is that you shortlist the models that have at least 70% accuracy for unsupervised and 80% for supervised. You should also look at the loss function, setting up the threshold, accuracy matrix, and sensitivity/specificity.    Evaluation Evaluate results Review process Determine the next steps   At this step, you’ll have to decide which model to select using tools like ROC Curves, the number of features, and also business feedback. You can find more reading on this topic here: I linked this blog as it’s written by a Data Scientist, with hands-on experience.   Deployment Plan deployment Plan monitoring and maintenance Produce final report Review project   While preparing for deployment, you should create a final report and document if the model met objectives, start monitoring model stability, and accuracy and when retrain should

Photo by Cameron Yartz: https://www.pexels.com/photo/crop-woman-with-piece-of-ice-6433968/

At this stage, you managed to create your first Python project and saved all the packages used in a virtual environment. The next step is to collaborate with other data professionals. For a smooth collaboration with your peers, now you have to create a requirements.txt file with all the Python packages used and their respective versions. Create the requirements.txt: To automatically create the file, run the below line in the terminal: pip freeze > requirements.txt File usage There are numerous situations when you’ll use a requirements.txt file: share a project with your peers; use a build system; copy the project to any other location; project documentation. One more command line to remember for the first 3 situations: pip install -r requirements.txt It’s that simple! This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!

2022 Python IDE for Data Science

Wait, what?? Why would you use R Studio as an IDE for running Python? There’s a simple answer to this question: this is the perfect Data Science IDE when you use R and Python together. You’ll find below the simple steps to help set up a project in R Studio so you can start using Python: Create an R Studio Project: Navigate and save your project: File -> New Project – > Existing Directory (linked to the GitHub folder if applicable) / New Directory ( if you work locally) .   Create and activate Python virtual environment Each project might require different versions of packages and this can be encapsulated in a virtual environment. You’ll have to create and select the virtual environment as the Python interpreter for the RStudio Project and then activate. Install virtual environment with pip install in the RStudio terminal window (initial setup): pip install virtualenv Create the virtual environment for the current project (initial setup): virtualenv environment_name Activate the virtual environment for the current project (initial setup): on Windows: environment_name\Scripts\activate.bat  on MAC: source environment_name\bin\activate Select Python interpreter Navigate and select your Python interpreter : Tools -> Global Options – > Python -> Select -> Virtual environments When you open the project, just remember to activate the environment: environment_name\Scripts\activate.bat    (Note: I participate in the affiliate amazon program. This post may contain affiliate links from Amazon or other publishers I trust (at no extra cost to you). I may receive a small commission when you buy using my links, this helps to keep the blog alive! See disclosure for details.)   If you’re new to RStudio , you can browse this book. Once you start coding, you might be also interested in reading: the 17 Clean Code standards to adopt NOW! “Freeze” your Python environment by creating the Requirements.txt file     This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!

toddler indoor activities 2 year old

In this post I am listing the scientific benefits of spending time outdoors and why it’s a good thing for both kids and adults. Now, I’ll write about how indoors can also be fun and educational for toddlers. When indoors, we have to be careful with the amount of screentime a toddler is exposed to. The American Academy of Pediatrics discourages media use by children younger than 2 and recommends limiting older children’s screen time to no more than one or two hours a day. (More on this here). When she has to stay indoors all day, Bianca, my eldest ( 2 years and 9 months today), has the below schedule: wakes up around 8 has breakfast at 9 morning activity ( cooking, painting) at 10 snack around 10:30 dancing at 11 independent play while mum prepares lunch 12 lunch time around 12:30 nap time 13-15 (1.5 hr – 2 hr) evening activities / English cartoons ( Romanian is her first language, but she knows plenty English words from books and cartoons) around 15:30 snack time at 16:00 independent play ( sensory activities: she likes water a lot) / crafts with mum at 17 dinner around 18 independent play at 19 bath time at  20 bedtime routine (reading 2/3 books and practicing English) at 20:30 bedtime between 20:30 and 21:00   Below, I have posted some videos and pics to show our indoor activities. They are super fun, but independent play is key! You should encourage your toddler to play by themselves daily ( in an incremental manner). They also need to explore the world with their tiny eyes. Make sure you give them toys age appropriate, so it won’t get dangerous, boring or frustrating when they play by themselves.   (Note: I participate in the affiliate amazon program. This post may contain affiliate links from Amazon or other publishers I trust (at no extra cost to you). I may receive a small commission when you buy using my links, this helps to keep the blog alive! See disclosure for details.)   Indoor Toddler Activity 1 – Cooking:   Our kitchen helpers: Little helper learning tower The perfect multi food processor, Bosch MUM   Indoor Toddler Activity 2 – Painting:  Toddler painting set    Indoor Toddler Activity 3 – Crafts: One of my few hobbies is creating interior design pieces with paint and lichen / moss. Bianca helps me and really enjoys the activity. I’ll create a separate post on it, but I’ll drop below some pics and what you need if you want to try it at home: lichen & moss glue gun and sticks acrylic paint paint brush set   Indoor Toddler Activity 4- Sensory activities: This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!

Are you ready to see the baby world from a data scientists’ perspective? I’m a mom since 2019 (upgraded to mom v2.0 in 2021) and a Data scientist since 2016 (check out my CV here). Hey. Wait. What’s a Data scientist? Anytime when someone asks me what I do for a living, I instantly think of Chandler and the below Friends scene:  via GIFER 🤣🤣🤣🤣🤣🤣🤣 A Data Scientist is a special kind of analyst that takes raw data, cleans it up and models it in order to be able to make predictions. I decided to create www.thebabydatascientist.com to promote data driven parenting, giving you the peace of mind every parent deserves with the aim of making your baby smile. If you happen to become interested in Data science, there will be a lot of information on this topic too for you to read. Ready to stay connected? This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!